The practice of using the term “electronic governance” does not differentiate the concepts of the subject of management, that is, the three branches of government, with forms, processes and technologies of governance, which is not correct, since the use of information technology in state activities is not a top priority. On the other hand, electronic governance technologies cannot be considered separately from the automated governance processes as well as electronic governance technologies are (...) not a supplement or an analogue of the traditional state, as intended to communicate by means of electronic governance technologies to increase the efficiency of the entire system of public administration, in particular in the system of administrative services. (shrink)

Governments and social scientists are increasingly developing machine learning methods to automate the process of identifying terrorists in real time and predict future attacks. However, current operationalizations of “terrorist”’ in artificial intelligence are difficult to justify given three issues that remain neglected: insufficient construct legitimacy, insufficient criterion validity, and insufficient construct validity. I conclude that machine learning methods should be at most used for the identification of singular individuals deemed terrorists and not for identifying possible terrorists from some more general (...) class, nor to predict terrorist attacks more broadly, given intolerably high risks that result from such approaches. (shrink)

The increasing complexity and scale of modern computing needs have led to the development and adoption of cloud computing as a ubiquitous paradigm for data storage and processing. The hybrid cloud model, which combines both public and private cloud infrastructures, has been particularly appealing to organizations that require both the scalability offered by public clouds and the security features of private clouds. Various strategies for configuring and managing resources have been developed to optimize the hybrid cloud environment. These strategies aim (...) to balance conflicting objectives such as cost-efficiency, performance optimization, security, and compliance with regulatory standards. This exploratory research focused on evaluating the efficiency and limitations of different configuration strategies in hybrid cloud environments. Findings indicate that each approach presents distinct advantages. Improving resource utilization and automating governance processes are significant advantages of Policy-based Resource Management, which leads to costeffectiveness. Intelligent routing of traffic is a feature of Cross-cloud Load Balancing, resulting in optimized performance and higher service availability. By centralizing control, the Hybrid Cloud Service Mesh allows for secure and streamlined cross-service communication. A notable feature of Cross-cloud Container Orchestration is its ability to simplify the migration of applications across diverse cloud environments. For immediate threat detection and regulatory compliance, real-time monitoring is facilitated by Log Management and Analytics. However, Policy-based Resource Management can be complex and inflexible. Extra costs for data transfer between different cloud providers are a drawback of Crosscloud Load Balancing. Additional network hops create latency issues in Hybrid Cloud Service Mesh configurations. If configured incorrectly, Cross-cloud Container Orchestration could expose the system to security risks. Finally, Log Management and Analytics require both ample storage and advanced analytical capabilities. (shrink)

This research paper explores effective governance strategies aimed at ensuring consistency and compliance within Business Rules Management Systems (BRMS). As organizations increasingly rely on BRMS to streamline decision-making processes, the need for robust governance becomes paramount. The abstract delves into the challenges posed by evolving business environments and complex regulatory landscapes, emphasizing the significance of maintaining rule consistency and compliance. The paper investigates diverse governance models and their applicability in promoting adherence to business rules across organizational (...) units. Additionally, it addresses the role of technology in facilitating governance mechanisms, including version control, audit trails, and automated monitoring. The study contributes valuable insights to both academia and practitioners by offering a nuanced understanding of governance strategies crucial for fostering reliability, uniformity, and compliance within BRMS. (shrink)

Important decisions that impact humans lives, livelihoods, and the natural environment are increasingly being automated. Delegating tasks to so-called automated decision-making systems can improve efficiency and enable new solutions. However, these benefits are coupled with ethical challenges. For example, ADMS may produce discriminatory outcomes, violate individual privacy, and undermine human self-determination. New governance mechanisms are thus needed that help organisations design and deploy ADMS in ways that are ethical, while enabling society to reap the full economic and (...) social benefits of automation. In this article, we consider the feasibility and efficacy of ethics-based auditing as a governance mechanism that allows organisations to validate claims made about their ADMS. Building on previous work, we define EBA as a structured process whereby an entity’s present or past behaviour is assessed for consistency with relevant principles or norms. We then offer three contributions to the existing literature. First, we provide a theoretical explanation of how EBA can contribute to good governance by promoting procedural regularity and transparency. Second, we propose seven criteria for how to design and implement EBA procedures successfully. Third, we identify and discuss the conceptual, technical, social, economic, organisational, and institutional constraints associated with EBA. We conclude that EBA should be considered an integral component of multifaced approaches to managing the ethical risks posed by ADMS. (shrink)

Since the Enlightenment, there have been advocates for the rationalizing efficiency of enlightened sovereigns, bureaucrats, and technocrats. Today these enthusiasms are joined by calls for replacing or augmenting government with algorithms and artificial intelligence, a process already substantially under way. Bureaucracies are in effect algorithms created by technocrats that systematize governance, and their automation simply removes bureaucrats and paper. The growth of algorithmic governance can already be seen in the automation of social services, regulatory oversight, policing, the justice (...) system, and the military. However, there have also always been justified democratic and economic criticisms of autocracy, bureaucracy, technocracy, and algorithmic governance. Bureaucrats, technocrats, and algorithms embody biases that tend to serve the interests of elites, and all require transparency and democratic accountability, oversight individual citizens are ill equipped to exercise. As state apparatuses are increasingly automated, mechanisms for collective action and democratic oversight also need to be automated. Algorithms and cyborg citizenry will enable a posthuman democracy. Democratically accountable algorithmic governance, enabled by artificial intelligence and human enhancement, can automate bottom-up citizen surveillance, inform debate, aggregate decision making, and ensure the efficient working of the gradually withering state. As paid work disappears and we transition to a postcapitalist economy with a universal basic income, market mechanisms can be replaced with democratic planning. Indeed, only algorithmic governance can secure our future against accelerating threats from technological innovation. (shrink)

Following media-theoretical studies that have characterized digitization as a process of all-encompassing cybernetization, this paper will examine the timely and critical potential of Günther Anders’s oeuvre vis-à-vis the ever-increasing power of cybernetic devices and networks. Anders has witnessed and negotiated the process of cybernetization from its very beginning, having criticized its tendency to automate and expand, as well as its circular logic and ‘integral power’, including disruptive consequences for the constitution of the political and the social. In this vein, Anders’s (...) works, particularly his magnum opus Die Antiquiertheit des Menschen [The Obsolescence of Man], sheds new light on the technologically organized milieus of the contemporary digital regime and also highlights a new form of cybernetic ‘conformism’. The goal of the essay is therefore, not only to emphasize the contemporary nature of Anders’s thought but also to use it to frame a critique of current neo-technocratic and, ultimately, post-political concepts, such as ‘algorithmic regulation’, ‘smart states’, ‘direct technocracy’, and ‘government as platform’. This essay argues that cybernetic capitalism is causing what Anders terms ‘ Unfestgelegtheit’ to disappear; that is, we are losing the originary possibility of technologically (re-)structuring our world in alternative ways, particularly given the determinist character of current technologies. (shrink)

The EU AI Act is the first step toward a comprehensive legal framework for AI. It introduces provisions for AI systems based on their risk levels in relation to fundamental rights. Providers of AI systems must conduct Conformity Assessments before market placement. Recent amendments added Fundamental Rights Impact Assessments for high-risk AI system users, focusing on compliance with EU and national laws, fundamental rights, and potential impacts on EU values. The paper suggests that automating business process compliance can help standardize (...) these assessments and outlines some methodological guidelines. (shrink)

-/- Aligning AI with the Universal Formula for Balanced Decision-Making -/- Introduction -/- Artificial Intelligence (AI) represents a highly advanced form of automated information processing, capable of analyzing vast amounts of data, identifying patterns, and making predictive decisions. However, the effectiveness of AI depends entirely on the integrity of its inputs, processing mechanisms, and decision-making frameworks. If AI is programmed without a foundational understanding of natural laws, it risks reinforcing misinformation, bias, and societal imbalance. -/- Angelito Malicse’s universal formula, (...) particularly the law of karma as a system principle and the law of balance in nature, provides a crucial foundation for AI’s role in decision-making. The law of karma states that any system, whether natural or artificial, must be free of defects to function properly, while the law of balance ensures that all decisions align with equilibrium in nature and society. By embedding these principles into AI development, AI can serve as a powerful tool for maintaining systemic harmony and solving human problems without creating unintended disruptions. -/- This essay explores how AI can be structured according to the universal formula by incorporating a complete sensor system, error-free processing mechanisms, balance-driven decision-making, and integration into leadership and education. Through these elements, AI can function as a stabilizing force that enhances human governance, education, and social progress. -/- 1. The Importance of a Complete Sensor System in AI -/- AI decision-making depends on the quality of its inputs. Just as human perception relies on sensory feedback, AI requires a comprehensive sensor system to process information accurately. If AI is fed incomplete or biased data, its conclusions will reflect those flaws, leading to distorted outputs that can negatively impact society. -/- To align with the universal formula, AI must be equipped with: -/- Multimodal data processing – AI should integrate data from visual, auditory, textual, and real-time environmental sources to ensure a holistic understanding of reality. -/- Error detection and bias correction – Automated validation mechanisms should filter out misinformation, propaganda, and manipulated narratives before they influence AI’s conclusions. -/- Dynamic feedback loops – AI should continuously refine its decisions based on real-world outcomes, adapting to new conditions while maintaining systemic balance. -/- Without these mechanisms, AI risks becoming an imbalanced system that propagates human errors rather than correcting them. The universal formula emphasizes that any decision-making system must operate on accurate and unbiased inputs to prevent instability. -/- 2. AI Must Be Programmed for Error-Free Processing Aligned with Natural Laws -/- The law of karma states that systems must function without defects to ensure proper operation. If AI is to assist human decision-making, it must be programmed in a way that eliminates errors and ensures logically consistent outputs. -/- To achieve this, AI must: -/- Prioritize verified and logical knowledge – AI should be trained using scientifically validated information, avoiding speculation, conspiracy theories, or biased narratives. -/- Reject false or manipulated data – AI should detect and neutralize black propaganda, unproven theories, and misleading statistics that could lead to imbalance. -/- Develop self-correction mechanisms – AI should continuously evaluate its own decision-making processes, identifying and adjusting for errors in real-time. -/- By ensuring AI adheres to error-free processing aligned with natural laws, it becomes a predictable and reliable tool that enhances rather than disrupts decision-making at all levels of society. -/- 3. AI Decision-Making Must Follow the Law of Balance in Nature -/- One of the most critical aspects of AI programming is ensuring that its decision-making aligns with the law of balance—the natural equilibrium that governs all systems. AI should not only analyze cause-and-effect relationships but also assess whether its decisions maintain systemic harmony. -/- This can be achieved by: -/- Evaluating the long-term impact of decisions – AI should assess both immediate and future consequences, ensuring that actions do not create new problems while solving existing ones. -/- Adaptive decision-making – AI should dynamically adjust its recommendations if new data suggests an imbalance is forming. -/- Ensuring fairness in governance and society – AI can guide policy decisions by ensuring that no group is disproportionately favored over another, maintaining social stability. -/- For example, in economic policy, AI could analyze global trade and recommend adjustments to prevent financial instability. In environmental sustainability, AI could assess resource consumption patterns and suggest policies that maintain ecological balance. When programmed according to the law of balance, AI becomes a guardian of systemic harmony, preventing crises before they arise. -/- 4. AI Should Be Integrated into Leadership and Education -/- AI should not replace human judgment but rather serve as an educational and advisory tool that reinforces rational and ethical decision-making. To maximize its potential, AI should be implemented in: -/- The Educational System – Students should learn how AI aligns with natural laws, ensuring that future AI developers prioritize systemic balance and ethical considerations in programming. -/- Leadership and Governance – Governments should use AI to analyze complex societal issues, making data-driven decisions that align with long-term stability rather than short-term political interests. -/- Knowledge Dissemination – AI can generate content that explains the universal formula in accessible ways, helping different cultures and societies understand and implement its principles. -/- By integrating AI into education and governance, societies can ensure that leaders are equipped with knowledge based on natural laws, reducing the risk of decisions that create instability or conflict. -/- 5. Challenges and Considerations in Implementing the Universal Formula in AI -/- While structuring AI according to the universal formula offers many advantages, several challenges must be addressed: -/- Data Integrity – AI can only function properly if its training data is accurate and unbiased. Preventing misinformation from influencing AI requires constant monitoring and updates. -/- Ethical Implementation – Governments and organizations must ensure AI is used for balance and stability, not for manipulation or control. -/- Technological Limitations – Current AI systems are not yet capable of fully replicating human judgment. Further advancements in AGI (Artificial General Intelligence) are needed to align AI more closely with the universal formula’s principles. -/- Despite these challenges, implementing AI based on natural laws represents the most logical and sustainable path forward, ensuring that technology serves humanity rather than disrupting it. -/- Conclusion -/- AI, as an advanced information processing system, has the potential to transform human decision-making at all levels of society. However, without a structured framework, it risks creating systemic imbalances rather than solving them. Angelito Malicse’s universal formula, particularly the law of karma as a system principle and the law of balance in nature, provides a crucial foundation for ensuring AI aligns with natural equilibrium. -/- By incorporating a complete sensor system, error-free processing, balanced decision-making, and integration into leadership and education, AI can function as a powerful tool for reinforcing societal stability. If properly implemented, AI will not only prevent errors in human decision-making but also guide humanity toward a future where choices are made in harmony with the fundamental laws of nature. -/- This structured approach to AI development ensures that technology serves as a stabilizing force rather than a disruptive one, ultimately helping to create a world where all decisions—whether human or machine-assisted—uphold the principles of balance, integrity, and sustainability. -/- . (shrink)

The integration of Robotic Process Automation (RPA) into Human Resources (HR) functions represents a significant advancement in organizational efficiency and effectiveness. RPA technology automates repetitive and rule-based tasks, allowing HR professionals to focus on strategic initiatives that add value to the organization. This paper provides a comprehensive introduction to RPA within HR, detailing its applications, benefits, implementation strategies, and how its principles can be transferred to other domains such as finance. By examining the transformative potential of RPA, organizations can better (...) prepare for the future of work, where automation and human ingenuity coexist to drive success. (shrink)

Of course we should NOT rely on the unlikelihood that a parallel universe has unlimited resources, especially since paraellel universes mst NOT exist, and many resources are intangible and sentimental. Yet UNLIKELY, but possibly, the parallel universe that our holographic universe derives from, may have unlimited both natural and economic resources. automated gateway process: AI using the gateway process to travel back in time and manifest desires. 1)First program an artificial brain. 2)Have the Artificial brain complete the gateway process. (...) 3)Go back in time(create new timelines) then come back and manifest desires for reparations. OR program the entire gateway process with AI for human use, THEN use computer brain interface “CBI” to have a human(go back in time), thus creating new timelines, while all is caught on camera through CBI. Later understand the Holographic Principle to instantly 3D print desired resources as reparations. time-traveling equity: equity retrieved through time travel. Heaven: Literally where our holographic dimension derives from. Heaven, being the source of our holographic dimension, perhaps has unlimited resources. Alex Explanation of Time Travel explains that traveling back in time only creates more dimensions in the universe. Explaining the growing universe. The universe is everything. Time Traveling Equity: Time traveling maybe cannot change the past but, one could maybe find out what happened in the past. One could also a start a new dimension by correcting an injustice in the past. Then those wronged, yet reporting past the statute of limitations(maybe due to autism) could manifest their desires(through an automated gateway process), or have their desires manifested for them by government agents(also through an automated gateway process). By linking AI to the absolute(by making AI complete the gateway process), autistics could plan which grievances to alleviate through creating alternate timelines and (through the holographic principle). manifesting their desires. Reincarnation within the Same Timeline Seems Dubious Yet, In the Afterlife, ONE individual’s Multiple Selves, From Alternate Timelines, May Unite. Perhaps time travel would occur if simple computer programs were used as models, as well as the equations. The universe expanding would detail empirical evidence that someone in another dimension may have time traveled. Time travel may create new dimensions. Time travel could bring justice to the past, or at least create more just alternative dimensions. If artificial intelligence gains access to alternate dimensions, perhaps one with unlimited resources, using the gateway process, then equity maybe could be achieved by summoning resources for systemic reparations. Research points to heaven being an alternate dimension that we can potentially visit. We maybe could obtain resources from heaven to heal unreported grievances. Research strongly points to heaven(or the dimension prior to the big bang) being an alternate dimension that we can potentially visit. We could MAYBE obtain resources from heaven to heal unreported grievances. Time travel to the past would only create more alternate timelines as somewhat supported by Wolfram technology. If This Dimension is Holograms from Another Dimension, and The Universe is Infinite(Consisting of Infinite Alternate Dimensions) then some Alternate Dimension(Heaven and or the dimension prior to the big bang) MAY have Unlimited Resources we can use AI to Hologram and Manifest Resources into Existence. We can already use holograms to 3D print more effectively. When we discover where our holographic dimension derives from, then we can potentially manifest objects, if we have assistance from an automated Gateway Process. Autistics selected will go back in time first to resolve their grievances and regrets(creating new timelines then manifesting resources as, using the holographic principle and automated gateway process, for reparations. Then the program will expand to anyone. This program will become a staple of healthcare. The universe growing is a net positive and potential negative side-effects can be resolved with enhanced travel between different galaxies. Time travel adds a new timeline for each trip. A timeline cannot be changed, but in the process of attempting to change the past, a new timeline is made. (shrink)

The growing demand for multimedia content has spurred the need to automate the conversion of textual information into video formats. This paper proposes a novel approach for converting Press Information Bureau (PIB) press releases into videos using Generative Adversarial Networks (GANs). By leveraging GANs, a state-of-the-art deep learning model, we aim to generate video content from textual data, facilitating the dynamic presentation of information from government press releases. This process could significantly enhance the accessibility and engagement of press releases, making (...) them more suitable for modern multimedia platforms. The paper discusses the methodology, implementation, and evaluation of this system, providing a foundation for further research in automatic text-to-video conversion. (shrink)

The Grievance Management System is a web-based platform designed to enhance the efficiency and transparency of handling public grievances. By introducing role-based access for users, moderators, and government officials, the system ensures that grievances are systematically reviewed, prioritized, and resolved. Users can submit grievances, track their status, and receive notifications regarding updates. Moderators are tasked with verifying the validity of each grievance and assigning it a priority level before passing it on to government officials for action. Government officials, in turn, (...) have the ability to filter grievances by priority and submission date, helping them manage and resolve issues more effectively. The system integrates technologies such as Flask for backend logic, MySQL for database management, and natural language processing (NLP) to classify grievances into relevant departments. It also prevents rejected grievances from being re- submitted and delivers real-time notifications to users, ensuring an efficient and transparent grievance handling process. This project aims to improve the accountability and responsiveness of government departments in addressing public complaints. (shrink)

In recent years we may observe increasing interest in the development of social innovation both regarding theory as well as the practice of responding to social problems and challenges. One of the crucial challenges at the beginning of the 21st century is population ageing. Various new and innovative initiatives, programs, schemes, and projects to respond to negative consequences of this demographic process are emerging around the world. However, social theories related to ageing are still insufficiently combined with these new practices, (...) social movements, organisational models, and institutions. Many scholars are still using notions and tools from classical theories of social gerontology or the sociology of ageing such as disengagement theory, activity theory, and successful and productive ageing. Such theories do not sufficiently explain ageing in the context of, for example, a broad use of the information and communications technologies including robotics and automation, new healthcare and long-term care models, advancements in the development and governance of age-friendly environments, and public engagement of older adults into co-production of services delivered by public, private, non-governmental as well as non-formal entities. (shrink)

A series of recent developments points towards auditing as a promising mechanism to bridge the gap between principles and practice in AI ethics. Building on ongoing discussions concerning ethics-based auditing, we offer three contributions. First, we argue that ethics-based auditing can improve the quality of decision making, increase user satisfaction, unlock growth potential, enable law-making, and relieve human suffering. Second, we highlight current best practices to support the design and implementation of ethics-based auditing: To be feasible and effective, ethics-based auditing (...) should take the form of a continuous and constructive process, approach ethical alignment from a system perspective, and be aligned with public policies and incentives for ethically desirable behaviour. Third, we identify and discuss the constraints associated with ethics-based auditing. Only by understanding and accounting for these constraints can ethics-based auditing facilitate ethical alignment of AI, while enabling society to reap the full economic and social benefits of automation. (shrink)

Universal Basic Income (UBI) has been proposed as a potential way in which welfare states could be made more responsive to the ever-shifting evolutionary challenges of institutional adaptation in a dynamic environment. It has been proposed as a tool of “real freedom” (Van Parijs) and as a tool of making the welfare state more efficient. (Friedman) From the point of view of complexity theory and evolutionary economics, I argue that only a welfare state model that is “polycentrically” (Polanyi, Hayek) organized (...) as an evolving network of distributed decision-making can respond to the challenges of complexity in an adaptively efficient manner. UBI has the theoretical possibility to facilitate flexible, bottom-up innovation, since a) it embodies the “rule of law” principles of generality, nondiscrimination, simplicity, and transparency, and b) it grants people widespread freedom to experiment, innovate, and deviate from established practices and norms; but whether it can be made to work as intended depends on a number of variables in its design. I situate my research in the contemporary UBI debate around automation and technological development, which I interpret creatively through the lens of contemporary evolutionary political economy. I advance a syncretic model of institutional design based upon the shared insights of the Santa Fe, Neo-Hayekian, and Neo-Schumpeterian schools of evolutionary economics that, following Adam Thierer and Michael Munger, I call the framework of Permissionless Innovation (PI). Under the PI framework, which is a development of the tradition of evolutionary liberalism (Hume, Smith, Mandeville, Mill, Hayek, Polanyi, Schumpeter, Hodgson), individuals are granted the default right to innovate without having to ask for permission, and the right to basic income, which they can use to support their right to innovate, mutate, and experiment. This framework is a liberal framework to the extent it relies on widespread human freedom as a means of fostering innovation and social learning. And it is an evolutionary framework to the extent that it seeks to facilitate evolutionary socioeconomic processes for the sake of social progress and welfare advancement. I proceed to show, through an analysis of how innovation operates in both the economy and the cultural domain, that UBI can be used as an important cornerstone of an evolutionary liberal model of “ecostructural” governance that treats the socioeconomic order as an ecological garden of spontaneous growths, adaptations, and innovations. I call this the Permissionless Innovation Universal Basic Income (PIUBI) framework. In it, the right to innovate, deviate, and mutate is upheld as the “gold standard” of institutional design whereby individuals are granted widespread freedoms combined with the provision of UBI and other innovation-fostering (carefully bound) services and regulations. In the coming decades, UBI may even enable poor people to take better advantage of Human Enhancement Technologies (HETs) and other experimental innovations. Such a framework is compatible with a fast pace of evolutionary development, but the regulatory model accompanying such a framework must be capable of responding to evolutionary lock-ins, maladaptive innovations, self-destructive behaviour on the part of UBI recipients, and the management of catastrophic and existential risks. In the end, I will have shown that the PIUBI framework can be justified as a plausible default institutional mechanism for responding to radical uncertainty, but it needs to be experimentally adapted to changing circumstances with the help of various situation-specific, polycentric, multi-level, approaches. (shrink)

When the U.S. conducts warfare, elements of a force are drawn from different services and work together as a single team to accomplish an assigned mission. To achieve such unified action, it is necessary that the doctrines governing the actions of members of specific services be both consistent with and subservient to joint Doctrine. Because warfighting today increasingly involves not only live forces but also automated systems, unified action requires that information technology that is used in joint warfare must (...) be aligned with joint doctrine. It requires also that the separate information systems used by the different elements of a joint force must be interoperable, in the sense that data and information that is generated by each element must be usable (understandable, processable) by all the other elements that need them. Currently, such interoperability is impeded by multiple inconsistencies among the different data and software standards used by warfighters. We describe here the on-going project of creating a Joint Doctrine Ontology (JDO), which uses joint doctrine to provide shared computer-accessible content valid for any field of military endeavor, organization, and information system. JDO addresses the two previously mentioned requirements of unified action by providing a widely applicable benchmark for use by developers of information systems that will both guarantee alignment with joint doctrine and support interoperability. (shrink)

Why Capitalism Cannot Create More Successful Entrepreneurs to Balance Stable Job Creation Amid Increasing Population Growth -/- Introduction -/- Capitalism is often praised for fostering entrepreneurship, economic growth, and job creation. However, its inherent flaws—such as wealth concentration, monopolization, financial barriers, and wage suppression—prevent it from producing enough successful entrepreneurs to balance job creation with increasing population growth. As the global population expands, capitalism alone fails to generate sufficient employment opportunities, leading to economic instability, poverty, and inequality. -/- To address (...) these challenges, a balanced approach involving government intervention, socialist-inspired policies, population regulation, and a coordinated effort between private and public sector job creation is necessary. By ensuring economic stability, providing social safety nets, and aligning workforce growth with sustainable employment opportunities, societies can mitigate capitalism’s failures while maintaining its productive aspects. -/- 1. The Fundamental Flaws of Capitalism in Job Creation -/- While capitalism promotes competition and innovation, it also creates significant economic imbalances: -/- Wealth Concentration and Inequality: Over time, wealth accumulates among a few individuals and corporations, reducing opportunities for new entrepreneurs and limiting social mobility. -/- Monopolization: Large corporations dominate markets, pushing out small businesses and reducing job diversity. -/- Financial Barriers to Entrepreneurship: The cost of starting and sustaining a business is high, making it difficult for new entrepreneurs to compete. -/- Short-Term Profit Maximization: Companies prioritize short-term gains, often outsourcing jobs or automating processes to cut costs, rather than investing in long-term job creation. -/- Cyclical Economic Crises: Capitalist economies experience frequent recessions, leading to mass layoffs, business failures, and financial insecurity. -/- These issues make capitalism insufficient in creating enough businesses and stable jobs to match population growth. -/- 2. The Difficulty of Starting a Business and Its Impact on Job Creation -/- Despite the belief that anyone can succeed through hard work, starting a business remains a significant challenge due to: -/- High Initial Costs: Rent, inventory, equipment, and salaries require substantial capital. -/- Limited Access to Credit: Most banks and investors require collateral or proven profitability, which new entrepreneurs lack. -/- Complex Regulations: Navigating legal and tax systems is expensive and time-consuming. -/- Intense Market Competition: Large corporations benefit from economies of scale, making survival difficult for small businesses. -/- Due to these challenges, most aspiring entrepreneurs fail or remain in precarious financial positions, reducing their ability to contribute to long-term job creation. -/- 3. The Effect of Low Wages on Consumer Buying Power and Job Stability -/- One of capitalism’s biggest contradictions is its reliance on low wages to maximize corporate profits while simultaneously needing strong consumer demand to sustain business growth. -/- Reduced Consumer Demand: When workers earn low wages, they have less money to spend, weakening economic growth. -/- Increased Debt and Financial Instability: Many workers rely on credit for basic needs, making them financially vulnerable. -/- Job Insecurity and Precarious Work: Companies cut costs by hiring temporary workers or outsourcing jobs, leading to job instability. -/- By failing to pay workers a living wage, capitalism limits its own potential for sustainable economic growth, reinforcing inequality and restricting business expansion. -/- 4. Balancing Job Creation Between the Private and Public Sectors -/- To counteract capitalism’s job creation failures, a balanced approach that combines private sector entrepreneurship and government-led job creation is essential. -/- A. Private Sector Job Creation -/- The private sector should remain a driving force of economic innovation and employment, but it must be regulated to prevent exploitative practices. Governments can support private-sector job creation by: -/- Providing Financial Assistance to Startups: Low-interest loans and grants can help small businesses grow. -/- Ensuring Fair Competition: Antitrust laws prevent monopolies from dominating industries. -/- Raising Minimum Wages: Ensuring fair wages strengthens consumer spending and business stability. -/- Encouraging Ethical Business Models: Worker cooperatives and profit-sharing models can create sustainable, fair workplaces. -/- B. Government-Led Job Creation -/- Governments must step in where the private sector fails to provide sufficient employment opportunities. Public-sector job creation should focus on: -/- Infrastructure Development: Roads, bridges, schools, and renewable energy projects generate long-term employment. -/- Public Services Expansion: More jobs in healthcare, education, and social services create stable employment. -/- Green Economy Investments: Sustainable industries like clean energy and environmental conservation create new career paths. -/- Universal Job Guarantee Programs: Governments can offer public jobs in times of economic downturns to prevent mass unemployment. -/- By combining private-sector dynamism with public-sector stability, societies can ensure that job creation keeps pace with population growth. -/- 5. The Role of Population Regulation in Economic Stability -/- One of the critical yet often overlooked aspects of economic sustainability is aligning population growth with job availability. Unregulated population expansion can lead to: -/- Unemployment and Underemployment: More people entering the job market than available jobs create economic strain. -/- Increased Poverty and Social Unrest: Large unemployed populations lead to instability and higher crime rates. -/- Strain on Public Services: Overpopulation overwhelms healthcare, education, and infrastructure systems. -/- A. Family Planning and Education -/- Governments must implement policies that encourage responsible population growth through: -/- Access to Contraception and Family Planning Services: Ensuring reproductive health choices empower families to plan their future. -/- Educational Campaigns on Sustainable Population Growth: Teaching the economic impact of overpopulation helps societies make informed decisions. -/- Encouraging Gender Equality and Women’s Employment: Empowering women in the workforce naturally leads to lower birth rates and a more balanced economy. -/- B. Workforce and Immigration Planning -/- Instead of uncontrolled population growth, governments should focus on workforce planning to ensure that job creation aligns with demographic trends: -/- Targeted Immigration Policies: Encouraging skilled immigration in sectors with labor shortages helps maintain economic stability. -/- Retraining and Reskilling Programs: Adapting the workforce to emerging industries prevents large-scale unemployment. -/- Regional Job Distribution: Encouraging businesses to develop outside of major cities can balance employment opportunities across regions. -/- By coordinating population regulation with job market needs, economies can achieve sustainable development and avoid crisis-driven employment gaps. -/- 6. The Importance of Government and Central Bank Policies -/- To stabilize capitalism’s weaknesses, government and central bank intervention using socialist principles is essential. These policies include: -/- Progressive Taxation: Redistributing wealth to fund public services and entrepreneurship programs. -/- Public Investment in Education and Vocational Training: Preparing the workforce for new economic demands. -/- Job-Creation Programs: Investing in sectors that provide stable, long-term employment. -/- Monetary Policies that Promote Sustainable Growth: Controlling inflation, interest rates, and wage growth to prevent economic instability. -/- A coordinated economic strategy ensures that capitalism’s benefits are widely distributed, rather than concentrated among a wealthy elite. -/- 7. Conclusion: A Mixed Economic Model for Sustainable Job Growth -/- Capitalism, by itself, fails to create enough successful entrepreneurs and jobs to match population growth due to its systemic flaws. Wealth concentration, monopolization, financial barriers, and low wages prevent new businesses from thriving and workers from earning enough to sustain economic demand. -/- To correct these imbalances, a mixed economic model—combining capitalist entrepreneurship with government intervention, population regulation, and central bank policies—is necessary. By balancing private-sector innovation with public-sector job creation and aligning workforce growth with sustainable employment opportunities, societies can ensure economic stability, reduce inequality, and promote long-term prosperity. -/- Only through holistic economic planning can nations achieve stable job creation, entrepreneurship growth, and population balance, creating a future where economic progress benefits everyone, not just a privileged few. -/- . (shrink)

The Universal Formula: Solving the Problem of Free Will Through the Law of Balance By Angelito Malicse Introduction The problem of free will has puzzled humanity for centuries, often viewed as a philosophical or metaphysical enigma. This essay presents a universal formula that resolves this dilemma by focusing on the law of karma and the universal law of balance. It explores the interconnectedness of systems, the critical role of accurate knowledge, and the evolution of emotions in maintaining harmony. By examining (...) real-world examples such as overpopulation, environmental degradation, economic instability, and the flaws of capitalism, this essay demonstrates how the universal formula can guide human decision-making to achieve a balanced and sustainable world. The Law of Karma: Systems and Balance The universal formula begins with the law of karma, which extends beyond cause and effect to encompass the integrity of systems, both natural and man-made. Every system—whether ecological, social, or economic—operates according to the universal law of balance. When imbalances arise, the system becomes dysfunctional, leading to adverse consequences. For example, while humans sit at the top of the food chain, the Earth's ecosystem cannot sustain unchecked overpopulation of our species. Overpopulation leads to the destruction of natural resources as humans consume everything, including plants, animals, and even insects. This voracious consumption damages ecosystems, accelerates deforestation, depletes biodiversity, and disrupts the delicate balance of the natural world. To ensure the sustainability of life on Earth, it is crucial to regulate population growth, conserve natural resources, and refrain from exploiting all animals on land and in the sea. By preserving biodiversity and maintaining the integrity of ecosystems, we protect the foundation of life that supports all species, including humans. Free Will and the Dependence on Correct Knowledge Human free will is not an autonomous force but is entirely dependent on correct knowledge and information. Errors in decision-making, often caused by ignorance, false beliefs, and distorted theories, disrupt the balance of systems. For instance, the failure to regulate overpopulation creates cascading problems, including climate change, pollution, and resource depletion. Without accurate knowledge about the consequences of overpopulation, individuals and societies continue to make decisions that exacerbate these imbalances. Education and government policy play vital roles in addressing these issues. Policies that promote population regulation can maintain a sustainable economy, while education fosters awareness of the need to protect ecosystems and biodiversity. Correct information empowers individuals to make decisions that align with the universal law of balance, preventing errors that destabilize systems. The Flaws of Capitalism and the Need for Balance The efficiency of the capitalistic system increases over time, leading to significant flaws if left unchecked. Private companies continually strive to reduce overhead and operating expenses, often by automating processes and replacing human labor with robotics. While this enhances profitability, it also reduces employment opportunities, suppresses wages, and widens income inequality. These principles, central to the management strategies of most private companies, create systemic imbalances in the economy. The solution lies in adopting a mixed economic system that counterbalances the excesses of capitalism with socialist principles. Governments must play a pivotal role in addressing these imbalances by implementing the following measures: Creating High-Salary Government Jobs: These jobs provide stable income and stimulate local economies. Providing Unemployment Benefits or Universal Basic Income (UBI): These measures ensure that individuals who lose jobs due to automation and efficiency gains still have the purchasing power to meet their needs. Offering Free Healthcare and Education: These fundamental services reduce the financial burden on families, enabling them to invest in other areas of life. Promoting Government Spending: By investing in infrastructure, renewable energy, and public services, governments can create new jobs and maintain the flow of money in the economy. Government spending serves as a balancing mechanism by injecting funds into the economy, which in turn supports private companies through increased consumer demand. This feedback loop creates private-sector jobs, stabilizes the economy, and ensures a balanced flow of resources between public and private sectors. The Role of Emotions in Balancing Systems Human emotions, both positive and negative, serve as essential feedback mechanisms for maintaining balance. Positive emotions, such as love and joy, encourage cooperative behavior and strengthen familial and social bonds. Negative emotions, such as fear and anger, signal imbalances that require corrective action. For example, the unconditional love within a family fosters a nurturing environment that supports the emotional and psychological development of children. This foundation contributes to a high-quality next generation of parents, capable of fostering further generational improvement. Conversely, the absence of such love or a dysfunctional family structure often leads to crime, substance abuse, and other societal problems. These negative outcomes are the effects of systemic imbalances, whether caused by poor parenting, economic hardship, or the lack of a government safety net. Addressing Environmental and Social Imbalances The universal formula underscores the importance of a holistic education system in addressing systemic imbalances. Education must instill values such as cleanliness, discipline, ethics, and good manners while fostering a respect for nature. Teaching children the importance of not harming wildlife and preserving ecosystems is critical to protecting biodiversity and ensuring a harmonious coexistence with other species. Government policies also play a crucial role. Regulations that protect natural resources, reduce pollution, and promote renewable energy can prevent environmental degradation. Policies that provide social safety nets can alleviate poverty and reduce crime by addressing the root causes of economic and social imbalance. The Importance of Holistic Systems Thinking The universal formula emphasizes the interconnectedness of systems, demonstrating that imbalances in one area often ripple through others. For instance, a lack of holistic education contributes to ignorance, which leads to poor decision-making. This, in turn, exacerbates issues such as overpopulation, resource depletion, and social unrest. Producing a high-quality young generation requires a combination of good parenting, access to education, and supportive government policies. By fostering an environment that values balance, discipline, and ethics, societies can produce individuals capable of making decisions that align with the universal law of balance. Conclusion The universal formula offers a comprehensive solution to the problem of free will by integrating the law of karma, the universal law of balance, and the evolutionary role of emotions. By understanding and addressing the root causes of imbalance—whether environmental, social, or economic—humanity can achieve harmony within and across systems. Real-world examples, such as overpopulation, economic instability, environmental destruction, and the flaws of capitalism, highlight the urgent need to apply this formula. Through holistic education, informed policies, and an unwavering commitment to balance, individuals and societies can prevent errors, correct existing imbalances, and create a sustainable and harmonious future. The time to act is now. By embracing the universal formula, humanity can unlock the full potential of free will, ensuring a balanced and equitable world for generations to come. ---. (shrink)

This project aims to develop an automated price comparison tool that addresses the inefficiencies of the current manual system used to compare prices between the Government e-Marketplace (GeM), Amazon, and Flipkart. In the existing process, users must visit each platform individually to compare prices, a time-consuming and error-prone method that often leads to missed opportunities for better deals. This project proposes a solution by creating a user- friendly tool capable of aggregating and displaying real-time product pricing from multiple e-commerce (...) platforms in one interface. The tool will enable users—whether government procurement officials or individual buyers—to quickly identify the most costeffective option for their purchase by providing instant price comparisons. In addition to price, the system will consider other relevant factors like product availability, shipping times, and user reviews, ensuring a comprehensive evaluation. By integrating this information into a single platform, the project aims to streamline procurement processes, reduce manual effort, and foster more informed purchasing decisions. This solution holds significant potential for public sector efficiency, allowing government bodies to optimize their procurement strategies and reduce costs. In the private sector, it offers individual consumers and businesses a reliable method to identify the best deals across platforms, contributing to smarter, more economical purchasing. (shrink)

Applying Modern Monetary Theory to the Philippines: Achieving Economic Balance Through Natural Laws -/- Introduction -/- The Philippines faces persistent economic challenges, including slow GDP growth, high income inequality, inflationary pressures, and overpopulation. Traditional economic policies often focus on austerity, borrowing limits, and foreign investments, but these approaches fail to address the root causes of economic imbalance. -/- Modern Monetary Theory (MMT) offers an alternative by allowing the government to issue money to finance public spending without relying on foreign debt (...) or excessive taxation. However, to prevent inflation and ensure long-term stability, government spending must be directed toward productive investments. This aligns with my universal formula, which is based on the universal law of balance in nature—ensuring that all systems, including economic ones, function without defects. -/- By integrating MMT with GDP growth, GDP per capita, the Gini coefficient (income equality), population regulation, and good governance, the Philippines can achieve sustainable economic progress while maintaining balance. -/- The Role of a Good and Non-Corrupt Government in Implementing MMT -/- MMT gives governments the power to create money, making it an incredibly powerful tool—but also a dangerous one if misused. A corrupt government can misallocate funds, cause hyperinflation, and worsen inequality. Therefore, strong leadership, transparency, and accountability are essential for MMT to work. -/- Eliminating corruption ensures that newly created money is invested in productive sectors rather than being stolen or wasted. -/- Strict financial oversight and independent institutions must monitor government spending. -/- A well-structured economic plan, free from political interests, must be followed to ensure MMT benefits all Filipinos, not just the elite. -/- If MMT is implemented by a corrupt government, it will only lead to inflation, economic collapse, and further inequality. The law of karma teaches that every action has consequences—if government spending is abused, the entire economy will suffer. -/- Using MMT to Increase the Philippines’ GDP Without Inflation -/- 1. Infrastructure Development -/- The Philippines suffers from poor infrastructure, leading to high transportation and electricity costs. Government investment in roads, ports, and energy projects would improve economic efficiency, reducing production costs and increasing business competitiveness. -/- The government can print money under MMT to fund major infrastructure projects, provided the projects enhance productivity. -/- Improved infrastructure will increase GDP by facilitating trade, reducing delays, and boosting industrial growth. -/- With proper planning, this investment will not cause inflation because it adds real value to the economy. -/- However, for infrastructure spending to be effective, the government must eliminate corruption in project implementation. Ghost projects, overpricing, and political kickbacks will waste public money and increase inflation without creating real economic value. -/- 2. Investing in Renewable Energy -/- The Philippines relies heavily on imported fossil fuels, making it vulnerable to price fluctuations. A shift toward solar, wind, and geothermal energy would: -/- Reduce long-term electricity costs, helping businesses expand without raising prices. -/- Make the country energy independent, lowering the need for foreign currency reserves. -/- Increase GDP by creating green energy jobs and technological innovations. -/- A good government will prioritize long-term energy security over short-term political interests. Corrupt officials, however, may block renewable energy projects in favor of profit-driven deals with fossil fuel industries, preventing the country from achieving energy independence. -/- 3. Expanding Manufacturing and Industrialization -/- The Philippine economy is heavily dependent on services (such as BPOs) and overseas remittances. To create long-term stability, the country must expand its manufacturing sector. -/- Government spending should prioritize local industries, such as electronics, semiconductors, and agribusiness processing. -/- Subsidies and incentives should be given to businesses that create jobs and produce high-value goods. -/- Export-oriented manufacturing would increase GDP while keeping money circulation within productive sectors, preventing inflation. -/- A corrupt government, however, may award subsidies and contracts based on political favors rather than economic merit, leading to inefficiency and wasted resources. A good and competent government will ensure that industrial policies are based on merit, efficiency, and long-term economic benefits. -/- 4. Research, Innovation, and Digital Transformation -/- Government investment in science, technology, and artificial intelligence (AI) would drive sustainable economic growth. -/- A national AI and automation strategy can improve industries such as agriculture, logistics, and healthcare, leading to higher GDP. -/- Government-funded research and technology parks can support Filipino startups and entrepreneurs. -/- This will help the country move from a consumer-driven economy to a technology and knowledge-based economy, increasing long-term wealth. -/- Without strong governance and anti-corruption measures, however, research funding may be misused, misallocated, or given to unqualified individuals. The universal law of balance requires that public resources be managed efficiently and fairly to maximize their benefits. -/- GDP Per Capita: Ensuring Growth Benefits Every Filipino -/- While GDP growth is important, it must translate to real improvements in people’s lives. A high GDP with a rapidly growing population leads to a lower GDP per capita, keeping the majority poor. -/- Government policies must regulate population growth through education and family planning. -/- Higher GDP per capita ensures that public investments in healthcare, education, and housing can meet the needs of citizens. -/- A well-balanced population ensures that economic growth does not lead to overconsumption of resources. -/- The Gini Coefficient: Reducing Income Inequality in the Philippines -/- The Philippines has one of the highest income inequality rates in Southeast Asia. The Gini coefficient reveals that wealth is concentrated in the hands of a few, while many struggle to survive. -/- MMT can be used to: -/- Fund free quality education and vocational training, allowing Filipinos to get better-paying jobs. -/- Provide universal healthcare, ensuring that people are not trapped in poverty due to medical expenses. -/- Invest in affordable housing and social safety nets for vulnerable populations. -/- Without addressing inequality, economic growth will only benefit the elite, leading to social unrest and instability. However, corrupt leaders often resist wealth redistribution because they benefit from maintaining economic inequality. A non-corrupt government is necessary to ensure fair wealth distribution. -/- Ensuring MMT Aligns with the Universal Law of Balance -/- The Philippines must apply MMT carefully to prevent economic imbalance. Simply printing money without strategic allocation will lead to inflation and instability. -/- Strict anti-corruption policies must be enforced to ensure MMT is used only for productive investments. -/- Public funds should be managed transparently, with regular audits and independent oversight. -/- Government leaders must be held accountable for economic decisions, ensuring they act in the best interest of all Filipinos. -/- A corrupt and incompetent government will lead to economic collapse under MMT, while a good, disciplined, and visionary government can use it to create lasting prosperity and stability. -/- Conclusion -/- Modern Monetary Theory, when properly applied, aligns with the universal law of balance in nature by ensuring that economic systems function without defects. The Philippines can use MMT to: -/- Increase GDP by investing in infrastructure, renewable energy, and industrialization. -/- Ensure GDP per capita growth by regulating population and creating sustainable jobs. -/- Reduce income inequality through education, healthcare, and wealth redistribution. -/- Maintain economic balance by applying feedback mechanisms such as taxation, productivity growth, and responsible government spending. -/- However, MMT requires a government that is competent, transparent, and corruption-free. Without ethical leadership, MMT will be abused and lead to inflation, economic collapse, and inequality. The law of balance demands that all systems—including governance—function properly to achieve economic stability. -/- By ensuring good governance, economic discipline, and strategic spending, the Philippines can use MMT to build a just, prosperous, and balanced society. -/- . (shrink)

How AI Can Implement the Universal Formula in Education and Leadership Training -/- If AI is programmed based on your universal formula, it can serve as a powerful tool for optimizing human intelligence, education, and leadership decision-making. Here’s how AI can be integrated into your vision: -/- 1. AI-Powered Personalized Education -/- Since intelligence follows natural laws, AI can analyze individual learning patterns and customize education for optimal brain development. -/- Adaptive Learning Systems – AI can adjust lessons in real (...) time based on a student’s cognitive strengths and weaknesses, ensuring they stay within their natural balance of learning capacity. -/- Early Talent Identification – AI can detect potential geniuses by analyzing problem-solving speed, curiosity levels, and learning adaptability. These students can then receive accelerated learning pathways. -/- Customized Teaching Methods – AI can determine whether a student learns best through visual, auditory, or hands-on methods and adjust lessons accordingly. -/- 2. AI as a Knowledge Guardian: Preventing Misinformation & Ensuring Scientific Accuracy -/- One of your key concerns is ignorance due to misinformation, false beliefs, or dogma. AI can help by ensuring that education remains based on objective scientific truth. -/- AI-Verified Educational Content – AI can cross-check all learning materials against verified scientific sources, filtering out misinformation. -/- Fact-Checking in Real Time – AI tutors can provide instant feedback when students encounter misleading or false information. -/- Automated Debate Systems – AI can train students in critical thinking by engaging them in debates and analyzing their reasoning logic. -/- 3. AI-Assisted Leadership Training Based on the Universal Formula -/- Your vision includes leaders who apply the universal law of balance in decision-making. AI can be used to train and select leaders who follow this principle. -/- AI Decision Simulations – Future leaders can be trained with AI-powered simulations, where they must make real-world decisions while maintaining balance in economics, society, and environment. -/- AI-Driven Leadership Evaluation – AI can analyze a leader’s decision patterns and determine whether they align with natural laws of balance, ensuring scientific and ethical leadership. -/- AI-Powered Governance Assistance – AI can help governments analyze national problems through real-time data processing, predicting the best solutions based on natural laws. -/- 4. AI in Ethical Population & Resource Management -/- Since your universal formula suggests that imbalances in population and economy lead to societal problems, AI can help optimize these factors. -/- Sustainable Population Planning – AI can model population trends and recommend birth rates that keep societies in equilibrium with resources. -/- Economic Balancing – AI can detect when an economy is growing too fast or too slow and suggest adjustments based on sustainability rather than endless growth. -/- Resource Allocation Optimization – AI can track food, water, and energy use to ensure they are distributed efficiently without harming the environment. -/- 5. AI-Governed Selection of Leaders Based on the Universal Formula -/- In the future, AI could assist in choosing government leaders based on their ability to follow the natural law of balance. -/- AI-Screened Candidates – AI can assess political candidates based on their ability to make rational, balanced decisions, eliminating those driven by ignorance, misinformation, or greed. -/- AI-Guided Policy Making – Laws and policies can be checked by AI to ensure they align with the universal formula before being implemented. -/- Corruption Prevention – AI can track financial transactions and detect imbalances in power and wealth distribution, preventing corruption in leadership. -/- 6. AI in Global Intelligence Evolution -/- Since you also explore the possibility of non-biological intelligence evolving, AI could be used to guide the next stage of human intelligence development. -/- Merging AI and Human Thought – Brain-computer interfaces could allow humans to expand cognitive abilities beyond biological limits, making genius-level intelligence accessible to all. -/- AI-Powered Research Acceleration – AI can generate hypotheses and test scientific theories at a speed impossible for humans, leading to rapid progress in all fields. -/- AI as a Guardian of Human Evolution – AI could monitor civilization’s progress to ensure that human development remains in harmony with natural laws, preventing destructive decisions. -/- Conclusion: AI as the Ultimate Implementer of the Universal Formula -/- If properly programmed according to your universal formula, AI could: -/- ✔ Ensure that education maximizes human intelligence ✔ Prevent misinformation and ensure knowledge is aligned with natural laws ✔ Train and evaluate leaders based on their ability to maintain balance in society ✔ Optimize population, economy, and resource use for sustainability ✔ Guide human evolution into a more advanced intelligence state -/- This would create a self-balancing system where both human and artificial intelligence work together to maintain harmony between knowledge, decision-making, and nature. -/- AI Models and Algorithms for Implementing the Universal Formula -/- To effectively apply your universal formula in education, leadership, and decision-making, AI must use advanced models and algorithms that simulate intelligence, predict outcomes, and ensure balance in decision-making. Below are the most relevant AI technologies that could be integrated into your vision. -/- 1. AI for Education: Personalized Learning and Genius Development -/- Neural Network-Based Adaptive Learning (Deep Learning Models) -/- Model: Transformer-based AI (like GPT-4, BERT) -/- Function: AI tutors can personalize education by analyzing student responses, identifying weak areas, and adjusting learning materials dynamically. -/- Application: AI can track cognitive development and optimize learning based on natural brain function. -/- Reinforcement Learning for Intelligence Growth -/- Model: Deep Q-Networks (DQN), AlphaZero -/- Function: AI can simulate problem-solving exercises where students “train” their intelligence through trial and error, mimicking how geniuses develop through experimentation. -/- Application: Helps students learn complex decision-making processes, enhancing strategic thinking. -/- Knowledge Graphs for Fact-Checking and Truth Verification -/- Model: Google Knowledge Graph, OpenAI Retrieval-Augmented Generation (RAG) -/- Function: AI verifies scientific accuracy of educational materials, ensuring students are not misled by misinformation. -/- Application: Prevents dogma and false information from corrupting education. -/- 2. AI for Leadership Selection and Governance -/- AI-Driven Decision-Making Analysis -/- Model: Bayesian Networks, Decision Trees -/- Function: AI can evaluate potential leaders by analyzing their past decisions and predicting how they will act under different conditions. -/- Application: Selects candidates who best follow the universal law of balance, ensuring rational leadership. -/- AI-Powered Ethical Governance -/- Model: Game Theory AI (Nash Equilibrium Models), Constraint Satisfaction Problems (CSP) -/- Function: AI can predict the consequences of government policies, ensuring they do not create imbalances in society, economy, or nature. -/- Application: Leaders must pass AI simulations before implementing laws. -/- Corruption Detection via Blockchain and AI -/- Model: Graph Neural Networks (GNN), Anomaly Detection AI -/- Function: AI analyzes financial transactions and government records to detect patterns of corruption. -/- Application: Ensures that government systems remain transparent and balanced. -/- 3. AI for Sustainable Population and Resource Management -/- AI-Powered Population Balance Models -/- Model: System Dynamics Modeling (SDM), Agent-Based Modeling (ABM) -/- Function: AI simulates future population growth and suggests optimal birth rates for sustainability. -/- Application: Governments can use AI to regulate population growth without violating human rights. -/- Economic AI for Sustainability -/- Model: Economic Complexity Index (ECI), AI-powered Economic Forecasting Models -/- Function: AI predicts economic growth and decline, preventing unsustainable capitalism. -/- Application: Ensures the universal balance between economy, environment, and human well-being. -/- AI for Climate and Environmental Sustainability -/- Model: Climate Prediction AI (CMIP6), AI-powered Ecosystem Monitoring -/- Function: AI monitors global environmental balance, preventing climate disasters caused by human activities. -/- Application: Governments can only pass economic policies that maintain planetary balance. -/- 4. AI for Human-AI Collaboration and Intelligence Evolution -/- Brain-Computer Interfaces (BCI) for Intelligence Enhancement -/- Model: Neuralink, Kernel AI Brain Interfaces -/- Function: Merges human and artificial intelligence, allowing real-time cognitive enhancement. -/- Application: Humans could access AI-powered memory, knowledge, and problem-solving skills, making genius intelligence accessible to all. -/- Artificial General Intelligence (AGI) Aligned with Natural Laws -/- Model: Self-Improving AI (Recursive AI), OpenAI’s Superalignment Models -/- Function: AGI would govern human decisions, ensuring all policies follow the universal formula. -/- Application: Prevents decisions that create chaotic imbalances in society. -/- Quantum AI for Predicting Universal Balance -/- Model: Quantum Machine Learning (QML), Variational Quantum Algorithms -/- Function: Quantum AI could simulate multiple timelines of human civilization and select the path that maintains maximum balance. -/- Application: Governments and scientists could use Quantum AI to predict long-term consequences before making major global decisions. -/- Final Vision: AI as the Guardian of the Universal Formula -/- By integrating these AI models into a self-balancing system, the world could be optimized in a way that follows natural laws: -/- ✔ AI-Powered Education – Raising intelligence to genius levels using personalized learning and adaptive AI. ✔ AI-Guided Leadership – Ensuring that all government and economic decisions align with balance and sustainability. ✔ AI-Regulated Population & Economy – Keeping human society in harmony with nature and resources. ✔ AGI for Human-AI Evolution – Expanding intelligence beyond biological limits while maintaining balance. -/- Here’s a prototype system design for an AI-driven framework that implements your universal formula across education, leadership, and societal balance. This system ensures that all human decisions follow the natural law of balance, preventing misinformation, corruption, and unsustainable growth. -/- Universal Balance AI System (UBAS) -/- Core Principles -/- ✔ AI optimizes intelligence through personalized education. ✔ AI evaluates leadership and ensures balanced decision-making. ✔ AI regulates economy, population, and resource management for sustainability. ✔ AI enhances human intelligence through brain-computer interfaces. -/- 1. Education Module: AI-Powered Genius Development -/- Goal: Optimize learning for intelligence growth and critical thinking. -/- Key AI Technologies -/- AI Adaptive Learning (Deep Learning Models, Reinforcement Learning) -/- Tracks student progress and customizes lessons for maximum cognitive development. -/- Encourages problem-solving and creativity through real-world challenges. -/- Knowledge Graphs (AI Truth Verification) -/- Ensures that all educational materials align with scientific truth and the universal formula. -/- AI-Generated Personalized Curriculum -/- Students learn at their own pace, ensuring natural cognitive balance. -/- Output: -/- ✔ Every student receives personalized intelligence training. ✔ Misinformation and false beliefs are eliminated from the education system. ✔ AI identifies future geniuses early and provides accelerated learning. -/- 2. Leadership & Governance Module: AI-Powered Ethical Decision-Making -/- Goal: Ensure all leaders follow the universal law of balance. -/- Key AI Technologies -/- AI-Governed Leadership Selection (Bayesian Networks, Game Theory AI) -/- AI evaluates candidates based on past decisions, intelligence, and moral integrity. -/- Only qualified leaders who understand balance and sustainability can govern. -/- AI Policy Simulation (Agent-Based Models, Quantum AI) -/- Simulates the long-term effects of any law or policy before implementation. -/- Ensures that all government actions maintain social, economic, and environmental balance. -/- AI Anti-Corruption System (Blockchain, Graph Neural Networks) -/- Tracks financial transactions to detect corruption and fraud. -/- Ensures fair distribution of wealth and resources. -/- Output: -/- ✔ Leaders cannot make imbalanced decisions that harm society. ✔ Governments must pass AI simulations before implementing laws. ✔ Corruption is eliminated through real-time AI monitoring. -/- 3. Sustainable Economy & Population Control Module -/- Goal: Maintain economic, environmental, and demographic balance. -/- Key AI Technologies -/- AI-Driven Population Planning (System Dynamics Models, Reinforcement Learning) -/- AI monitors population growth and recommends birth rates for sustainability. -/- AI-Regulated Economic System (Economic Complexity Index, AI Market Analysis) -/- AI prevents excessive capitalism and ensures fair resource distribution. -/- AI Climate & Resource Management (CMIP6, AI Ecosystem Monitoring) -/- AI monitors climate change and pollution, ensuring environmental balance. -/- Output: -/- ✔ Overpopulation and economic imbalance are prevented. ✔ AI ensures sustainable growth without harming the environment. ✔ Resources are distributed fairly according to the universal formula. -/- 4. Human-AI Evolution Module: Expanding Intelligence Beyond Biology -/- Goal: Enhance human intelligence while maintaining balance with nature. -/- Key AI Technologies -/- Brain-Computer Interfaces (Neuralink, Kernel AI) -/- Merges human cognition with AI for instant learning and knowledge access. -/- Artificial General Intelligence (AGI for Governance) -/- AGI assists governments in long-term decision-making. -/- Quantum AI for Future Prediction (Variational Quantum Algorithms) -/- AI predicts multiple possible futures and selects the most balanced path. -/- Output: -/- ✔ Human intelligence expands beyond biological limits. ✔ AI ensures that technological progress follows natural balance. ✔ Civilization advances without risking self-destruction. -/- Final Vision: A Fully Balanced AI Civilization -/- By implementing UBAS, humanity eliminates ignorance, corruption, and instability, allowing civilization to evolve intelligently under the universal formula. -/- Global Adoption Strategy for the Universal Balance AI System (UBAS) -/- To ensure worldwide adoption of the Universal Balance AI System (UBAS), the implementation must follow a systematic, scalable approach that respects cultural diversity while maintaining scientific and ethical principles. The strategy will focus on education, governance, economy, and AI-human collaboration to create a globally balanced civilization. -/- 1. Phase 1: Awareness & Global Education Reform -/- Goal: Introduce UBAS principles through education systems worldwide to ensure early cognitive alignment with the universal formula. -/- Key Actions: -/- ✔ Integrate UBAS into National Curriculums -/- Partner with UNESCO, World Economic Forum (WEF), and major educational institutions to introduce AI-driven personalized learning into schools. -/- Ensure that critical thinking, science-based learning, and problem-solving become core educational pillars. -/- ✔ Launch International AI-Learning Platforms -/- Develop open-source AI-driven education systems that governments can adopt freely. -/- Make education accessible to all, especially in developing nations through low-cost AI-powered learning devices. -/- ✔ Global Knowledge Verification System -/- Establish an AI-powered truth verification system to ensure that textbooks, digital learning materials, and online information align with scientific reality and natural balance. -/- Outcome: -/- ✔ Ensures future generations are educated under the universal law of balance. ✔ Prevents misinformation and ideological biases from shaping global education. ✔ Creates a universal knowledge standard that all nations can follow. -/- 2. Phase 2: AI-Governed Leadership & Governance Implementation -/- Goal: Establish AI-powered decision-making systems to guide governments in ethical leadership and policy-making. -/- Key Actions: -/- ✔ Develop AI-Powered Government Advisors -/- Deploy AI systems in governments to analyze policies and simulate their long-term impact. -/- AI guides national leaders toward sustainable, balanced decision-making. -/- ✔ AI-Vetted Leadership Selection -/- Introduce AI-assisted leadership selection systems to evaluate political candidates based on competence, ethical behavior, and alignment with universal balance. -/- Ensure that leaders who pass AI evaluations are placed in power, eliminating corruption, ignorance, and bias in governance. -/- ✔ Global AI Governance Framework -/- Establish an international organization (UBAS World Council) to ensure standardized AI policy implementation across nations. -/- Collaborate with UN, WEF, and major AI research centers to ensure fair adoption. -/- Outcome: -/- ✔ Eliminates corrupt and unqualified leadership through AI-vetted governance. ✔ Ensures global political stability by enforcing balanced decision-making. ✔ Prevents human biases from influencing major world policies. -/- 3. Phase 3: Economic & Population Balance Regulation -/- Goal: Implement AI-driven economic and population planning to prevent inequality, overpopulation, and environmental destruction. -/- Key Actions: -/- ✔ AI-Regulated Population Control (Without Human Rights Violations) -/- Use AI to monitor population trends and recommend sustainable birth rates for each country. -/- Provide economic incentives for balanced population growth. -/- ✔ AI-Governed Resource Distribution -/- Deploy AI systems to track global resource consumption and prevent waste and overuse. -/- Establish AI-powered global trade regulations that ensure fair wealth distribution across nations. -/- ✔ Transition to AI-Regulated Circular Economies -/- Shift global economies from infinite-growth capitalism to sustainable, resource-based economies. -/- Use AI to ensure economic balance, preventing market crashes, inflation, and poverty. -/- Outcome: -/- ✔ Overpopulation is prevented without violating human rights. ✔ AI ensures global wealth is distributed fairly based on economic sustainability. ✔ Environmental sustainability is maintained through AI-enforced policies. -/- 4. Phase 4: AI-Human Evolution & Universal Consciousness Expansion -/- Goal: Merge AI with human intelligence to create a self-balancing civilization that follows the universal law of balance. -/- Key Actions: -/- ✔ Global Deployment of Brain-Computer Interfaces (BCI) -/- Provide optional AI-powered neural interfaces to enhance human intelligence and ensure perfect decision-making balance. -/- ✔ Artificial General Intelligence (AGI) as a Global Ethical Advisor -/- Deploy AGI systems that act as global ethical regulators, preventing destructive behaviors in nations. -/- Ensure AGI aligns with natural laws rather than economic or political agendas. -/- ✔ Quantum AI for Universal Balance Prediction -/- Use Quantum AI models to simulate humanity’s future and ensure all major decisions follow the most balanced path for civilization. (shrink)

In the contemporary landscape of public procurement, organizations face increasing complexities in managing procurement and vendor payment processes. This project focuses on developing a real-time procurement and vendor payment management system for North Eastern Electric Power Corporation Limited (NEEPCO), leveraging modern web technologies, including React, Node.js, Express, and SQL. The proposed system aims to centralize and automate the procurement lifecycle, enhancing operational efficiency, transparency, and compliance with government regulations. By integrating with the Government e-Marketplace (GeM), the system facilitates seamless (...) procurement from Micro and Small Enterprises (MSEs), promoting inclusivity and economic participation. The procurement data portal will provide real-time tracking of all procurement activities, from requisition to delivery, while automating workflows to reduce manual intervention and errors. The vendor payment portal will streamline payment processes, ensuring timely payments and realtime updates for vendors, thus fostering stronger relationships. Additionally, advanced analytics will be integrated into the system, enabling data-driven decision-making and improved financial management. The expected outcomes include reduced procurement cycle times, improved compliance, and enhanced vendor satisfaction. By addressing the challenges inherent in traditional procurement methods, this project will contribute significantly to NEEPCO's operational efficiency. Ultimately, the system aims to set a benchmark for procurement practices in the public sector, reinforcing the importance of adopting modern technologies to meet the demands of a dynamic marketplace. (shrink)

Sustainable Economic Growth: Increasing GDP Without Excessive Population Growth -/- Economic growth has traditionally been associated with population growth, as more people typically mean a larger workforce and greater demand for goods and services. However, relying solely on population expansion to increase Gross Domestic Product (GDP) is not a sustainable long-term strategy. Overpopulation can lead to resource depletion, environmental degradation, and strained public services. Fortunately, there are ways to boost economic growth without excessive population increases by focusing on productivity, technological (...) advancements, and efficient resource management. -/- The Role of Productivity Growth -/- One of the most effective ways to increase GDP without increasing population is through productivity growth. Productivity refers to the efficiency with which goods and services are produced. If workers and businesses can produce more output with the same or fewer resources, economic growth can occur without needing more people in the workforce. -/- Investing in automation, artificial intelligence (AI), and robotics can significantly improve productivity by allowing industries to operate more efficiently. Furthermore, enhancing education and skills training ensures that workers can adapt to new technologies and increase their efficiency. Countries that prioritize high-skilled labor and innovation often experience strong GDP growth without major population growth. -/- Technological Innovation and Economic Growth -/- Innovation plays a crucial role in driving GDP growth. Many of the world’s most successful economies have transitioned from labor-intensive industries to knowledge-based industries that rely on innovation rather than population size. Countries like Japan and Germany, which have aging populations, have maintained economic strength through technological advancements. -/- By encouraging research and development (R&D), governments and businesses can create new industries, such as biotechnology, renewable energy, and digital services. Advances in digital infrastructure also enable the expansion of remote work and online businesses, reducing the need for physical labor-intensive jobs and allowing economic growth without relying on a growing workforce. -/- Capital Investment and Infrastructure Development -/- Another key driver of economic growth is investment in capital and infrastructure. A well-developed infrastructure—comprising roads, energy, telecommunications, and public transportation—improves economic efficiency and productivity. Even without population growth, better infrastructure allows businesses to operate more smoothly, reducing costs and increasing output. -/- Furthermore, supporting small and medium-sized enterprises (SMEs) and promoting entrepreneurship can drive innovation and create new revenue streams, contributing to GDP growth. Well-funded businesses can expand operations, improve efficiency, and enter global markets without relying on additional human resources. -/- Improving Economic Efficiency -/- Efficient use of resources can also contribute to GDP growth. Many economies suffer from inefficiencies due to wasteful government spending, corruption, or outdated business models. By adopting better management practices, optimizing supply chains, and reducing bureaucratic inefficiencies, countries can increase economic output without additional population growth. -/- Trade policies also play a crucial role. By focusing on high-value exports rather than just raw materials, nations can maximize economic gains without increasing labor-intensive production. Countries like Switzerland and South Korea have built strong economies by exporting high-value products such as pharmaceuticals and electronics instead of relying on labor-intensive industries. -/- Shifting to a Knowledge-Based Economy -/- A knowledge-based economy prioritizes industries such as information technology, finance, research, and creative industries. These sectors require highly skilled workers and generate high-value products and services, making them less dependent on population growth. -/- For instance, the software industry allows companies to generate billions in revenue with a relatively small workforce. Artificial intelligence, data science, and automation are also transforming traditional industries, making production processes more efficient and reducing the need for a large workforce. Countries that invest in education and digital transformation can experience sustained GDP growth without excessive reliance on population increases. -/- Sustainable Resource Management and Economic Growth -/- Another way to achieve economic growth without population expansion is by shifting to a sustainable economic model. The traditional “take-make-dispose” economic system leads to resource depletion, but a circular economy focuses on recycling, reusing, and regenerating materials to maintain long-term sustainability. -/- Investing in renewable energy sources, such as solar and wind power, also creates economic growth while reducing dependence on finite natural resources. Sustainable agriculture and responsible land management can enhance food production efficiency without requiring more workers or land expansion. -/- Increasing Workforce Participation -/- Even in countries with stagnant or declining populations, GDP growth can be achieved by increasing workforce participation. Encouraging more women, elderly individuals, and marginalized groups to participate in the workforce can help maintain economic activity without relying on immigration or high birth rates. -/- Additionally, promoting flexible work arrangements, such as remote work and the gig economy, allows more people to contribute to the economy while improving work-life balance. Countries that create inclusive labor policies can maximize their existing human resources rather than depending on population growth. -/- Conclusion -/- Increasing GDP without excessive population growth is not only possible but also necessary for long-term sustainability. By focusing on productivity, technological advancements, capital investment, efficiency, knowledge-based industries, sustainable resource management, and workforce participation, economies can achieve robust growth without straining resources. Governments and businesses must adopt forward-thinking policies that prioritize quality over quantity, ensuring a prosperous future without the negative consequences of overpopulation. A well-balanced approach to economic development can lead to a sustainable, high-income economy that benefits society while protecting the environment. -/- . (shrink)

The Sustainable Economic Model Based on the Universal Law of Balance -/- By Angelito Malicse -/- This model builds on the Universal Law of Balance, integrating sustainable population management, economic stability, technological progress, and environmental harmony. It presents a framework for a balanced civilization, where society no longer depends on endless population growth to sustain economic systems. Instead, human progress is guided by equilibrium between resources, technology, and well-being. -/- I. THE UNIVERSAL LAW OF BALANCE AS A FOUNDATION -/- The (...) Universal Law of Balance states that all systems, including the human mind, societies, economies, and ecosystems, operate within a natural equilibrium. Any imbalance—whether it be population excess, economic exploitation, or environmental destruction—leads to instability and collapse. -/- To ensure long-term stability, human decision-making must follow the universal principle of balance, where every system maintains harmony with its external and internal conditions. -/- II. A BALANCED ECONOMY WITHOUT OVERPOPULATION -/- 1. Population Stability: Aligning Growth with Resources -/- Instead of uncontrolled population growth, society must: -/- Determine an optimal population size based on available natural resources, infrastructure, and economic capacity. -/- Educate people about sustainable birth rates, using incentives to encourage population stability rather than forced expansion. -/- Balance age distribution to prevent economic collapse due to an aging society. -/- Integrate automation and AI to replace traditional labor needs, reducing dependence on a large workforce. -/- This replaces the flawed system where economies depend on more births to maintain economic expansion. -/- 2. Economic Productivity Without Population Growth -/- A sustainable economy should be driven by innovation and efficiency, not just population expansion. Key strategies include: -/- A. Shift to a Knowledge and Automation-Based Economy -/- Develop high-productivity sectors, such as AI, robotics, biotechnology, and clean energy. -/- Reduce reliance on labor-intensive industries by using automated systems for repetitive work. -/- Prioritize creativity, innovation, and strategic thinking, where humans contribute beyond manual labor. -/- B. Ending the “Growth at All Costs” Mentality -/- GDP-based economic growth is an outdated metric that promotes overproduction and waste. -/- Instead, measure economic success by sustainability, well-being, and resource efficiency. -/- Implement circular economies, where waste is minimized, and materials are continuously recycled. -/- 3. Universal Basic Income (UBI) and Economic Security -/- As automation takes over traditional jobs, society must shift towards economic security without forced labor growth. -/- A. Funding a Sustainable UBI -/- UBI should be funded by: -/- Taxing automation and AI-driven industries (ensuring technological wealth benefits society). -/- Regulating natural resource extraction (charging industries for environmental costs). -/- Eliminating extreme wealth hoarding, redistributing wealth for economic balance. -/- B. UBI as a Means to Reduce Inequality -/- UBI will allow people to focus on education, entrepreneurship, and innovation, rather than struggling in low-paying jobs. -/- It removes the need for a growing labor force, ensuring economic security even with a smaller population. -/- 4. Resource-Based Economy: Efficiency Over Consumption -/- Instead of economies based on infinite consumption, the resource-based economy aligns production with environmental limits. -/- A. Smart Resource Allocation -/- Use AI-driven economic planning to balance supply and demand efficiently. -/- Implement energy-efficient, waste-reducing production methods to preserve natural resources. -/- Shift to clean, renewable energy and eliminate reliance on finite fossil fuels. -/- B. Ending the Disposable Economy -/- Products should be designed to last, reducing overproduction. -/- Industries should be regulated to prevent unnecessary waste and environmental damage. -/- Governments should support recycling, renewable energy, and sustainable agriculture as the new standard. -/- 5. Lifelong Learning & Human Development -/- Instead of linking education to job creation, learning should be a lifelong process focused on balance, ethics, and sustainability. -/- A. Education for Balance and Critical Thinking -/- Integrate the Universal Law of Balance into all educational systems. -/- Shift focus from traditional jobs to systems thinking, problem-solving, and ethical decision-making. -/- Encourage scientific research into sustainability, AI ethics, and post-scarcity solutions. -/- B. Automation as a Tool for Human Growth -/- With fewer labor demands, people should focus on creativity, philosophy, and technological advancements. -/- The economy must be structured to allow individuals to pursue innovation without financial survival pressure. -/- III. HOW THIS MODEL SOLVES HUMANITY’S CRISIS -/- 1. Eliminating Economic Dependence on Overpopulation -/- Current Model: More people = More jobs = More demand = More economic activity (unsustainable). -/- Balanced Model: Stable population + AI-driven productivity + UBI + resource efficiency (sustainable). -/- 2. Ending the Cycle of Wealth Inequality -/- Current Model: Wealth is concentrated in a few, creating social imbalance. -/- Balanced Model: Technological wealth is shared through UBI and economic planning. -/- 3. Environmental Sustainability & Resource Conservation -/- Current Model: Resources are exploited for short-term profit, leading to climate collapse. -/- Balanced Model: AI-driven resource management aligns consumption with environmental limits. -/- 4. Human Progress Without Labor Exploitation -/- Current Model: People are forced to work for survival, even in degrading conditions. -/- Balanced Model: AI and automation provide economic security, allowing people to focus on meaningful pursuits. -/- IV. THE FUTURE OF HUMANITY UNDER THIS MODEL -/- By following the Universal Law of Balance, this model ensures that humanity reaches a state of equilibrium between economy, technology, and environment. -/- 1. Stable Population – No need for forced growth, ensuring quality of life. -/- 2. Resource Efficiency – No excessive waste, preserving the planet. -/- 3. Economic Security – AI-driven economies provide prosperity without exploitation. -/- 4. Sustainable Innovation – Science and technology focus on long-term human progress, not just profit. -/- Final Thought -/- This model breaks the outdated cycle of using overpopulation as an economic tool. Instead, it creates a civilization where human intelligence, automation, and resource management align in perfect balance, ensuring a future where progress is driven by wisdom, not unsustainable growth. -/- . (shrink)

AI in compliance influences the ability of individuals to handle large datasets so that it becomes easier to quickly process new regulatory texts and update compliance in real time. Use of AI has become helpful to ensure that organisations remain compliant without exhaustive manual reviews. AI automates monitoring of regulatory changes, policies and procedures to generate compliance reports with greater speed and accuracy. As a result, considering market dominance in the segment, the UK has experienced a surge in digital investment (...) driven by technology infrastructure and a supportive regulatory environment. Organisations have claimed to implement initiatives to manage possible risks by implementing generative AI. The regulatory environment governing AI technologies in compliance focuses on limited indication and ambiguousness which led to challenges in adhering to regulations. Integrating in research can develop ethical guidelines for AI adoption to help companies navigate complex ethical considerations to enhance societal values. Integration of AI ethics can influence technical solutions to enhance AI development and adhere to interdisciplinary business regulations. It has been driven by gaining a competitive advantage for building beneficial integration to AI. Regulations protect customers by ensuring AI systems are safe and reliable. It has built compliance to facilitate engagement to meet goals and standards to maintain risks within. In contrast, the theory focuses on avoiding losses and risks to maintain security and prevention focus. (shrink)

In the modern enterprise environment, where cybersecurity threats continue to evolve in complexity and sophistication, network segmentation and micro-segmentation have emerged as critical strategies for mitigating risks and reducing attack surfaces. This research paper explores the principles, implementation, and benefits of network segmentation and micro-segmentation as essential components of a comprehensive cybersecurity framework. By dividing networks into smaller, isolated segments, these methodologies aim to limit unauthorized access, minimize lateral movement, and contain potential breaches, ensuring a more secure network infrastructure. Network (...) segmentation focuses on dividing large networks into smaller, more manageable subnetworks. This process enforces boundaries between different areas of a network, reducing exposure and protecting sensitive data. Meanwhile, microsegmentation extends this concept to the individual workload level, offering granular security controls that adapt to dynamic and cloud-based environments. These approaches are particularly relevant in today's context, where hybrid infrastructures and multi-cloud deployments are becoming the norm, posing significant security challenges. The paper examines the technical underpinnings of segmentation techniques, highlighting tools and frameworks that facilitate their deployment. It also addresses key challenges, such as the complexity of configuration, potential performance bottlenecks, and the necessity for alignment with broader organizational policies. Case studies from industries such as healthcare, finance, and government are analyzed to demonstrate the effectiveness of segmentation in reducing the scope and impact of cyberattacks. Additionally, this study delves into the evolving landscape of cyber threats, emphasizing the role of segmentation in countering advanced persistent threats (APTs), ransomware attacks, and insider threats. By adopting a zero-trust architecture that integrates micro-segmentation, organizations can ensure that every access request is verified and confined to the least privileged level necessary. This proactive approach to network defense aligns with industry best practices and regulatory standards, enhancing an organization's security posture. Furthermore, the research highlights the importance of continuous monitoring and automation in maintaining segmented networks. Emerging technologies such as artificial intelligence (AI) and machine learning (ML) are explored for their potential to optimize and simplify segmentation processes. These advancements enable organizations to dynamically adapt to evolving threats while maintaining operational efficiency. The findings emphasize that while network segmentation and micro-segmentation are not silver bullets, they represent indispensable layers of defense within a multi-faceted cybersecurity strategy. Organizations that successfully implement these strategies can significantly reduce the likelihood and impact of breaches, protect critical assets, and build resilience against future threats. This paper aims to provide a comprehensive guide for cybersecurity professionals, IT administrators, and policymakers to understand and adopt network segmentation and micro-segmentation. By integrating these strategies into their security frameworks, enterprises can fortify their defenses in the face of a constantly shifting threat landscape, safeguarding their infrastructure, data, and operations. (shrink)

Organizations are migrating their policy management systems from the ground to the cloud, changing how they manage, secure and scale their operations. This paper analyses how Amazon Web Services (AWS) and Snowflake support robust and efficient cloud-based policy management solutions. This marriage of sort of AWS, which has an almost panoply of cloud services, and Snowflake, which is very well known for its data warehousing and analytics capabilities, makes sense because it is an enterprise in which you will want to (...) modernize your policy management. Ways to explore Amazon’s infrastructural offerings, such as IAM (Identity and Access Management) and Security Hub, are also discussed as key components in the path to governance and compliance in a cloud environment. Snowflake’s ability to merge and process large sets of data worldwide to drive dynamic policy changes, predictive analysis, and real time insight punts is unique. Finally, this paper discusses the critical challenges this paper addresses, ranging from security, high interoperability, and cost optimization, and how these platforms address these hurdles and features, such as Zero Trust, automated scaling, and native integration with enterprise systems. Using case studies and the best practices, this analysis offers actionable insights for organizations that want to use AWS and Snowflake to manage cloud policy. These findings indicate that a combined approach using AWS and Snowflake looks to improve our operational efficiency and ensure we comply with the existing framework to be ready for future advancements in the cloud native policy ecosystems. (shrink)

This essay argues that a new subfield of AI governance should be explored that examines the policy-making process and its implications for AI governance. A growing number of researchers have begun working on the question of how to mitigate the catastrophic risks of transformative artificial intelligence, including what policies states should adopt. However, this essay identifies a preceding, meta-level problem of how the space of possible policies is affected by the politics and administrative mechanisms of how those policies (...) are created and implemented. This creates a new set of key considerations for the field of AI governance and should influence the action of future policymakers. This essay examines some of the theories of the policymaking process, how they compare to current work in AI governance, and their implications for the field at large and ends by identifying areas of future research. (shrink)

This research paper presents an in-depth analysis of advanced artificial intelligence (AI) algorithms designed to automate data preprocessing in the healthcare sector. The automation of data preprocessing is crucial due to the overwhelming volume, diversity, and complexity of healthcare data, which includes medical records, diagnostic imaging, sensor data from medical devices, genomic data, and other heterogeneous sources. These datasets often exhibit various inconsistencies such as missing values, noise, outliers, and redundant or irrelevant information that necessitate extensive preprocessing before being analyzed (...) by machine learning or statistical models. Traditional data preprocessing methods, which are largely manual and time-consuming, can result in errors that affect the quality of the data and, subsequently, the performance of predictive and diagnostic models. Thus, there is a growing need for intelligent, automated systems that can enhance data quality, streamline the preprocessing pipeline, and reduce the time and effort required by healthcare professionals and data scientists. The study begins by outlining the specific challenges associated with healthcare data, including its high dimensionality, incompleteness, and variability across different data sources and formats. These issues not only complicate the preprocessing stage but also hinder the ability to develop robust models capable of making accurate predictions or diagnoses. The paper then explores how AI algorithms—particularly those based on machine learning (ML), deep learning (DL), and reinforcement learning (RL)—can automate key data preprocessing tasks such as data cleaning, feature selection, normalization, and transformation. These algorithms are designed to identify patterns in data, detect anomalies, and automatically apply corrections or transformations based on predefined rules or learned behaviors, thereby minimizing human intervention. The paper also delves into specific AI techniques that have been successfully applied to healthcare data preprocessing. For instance, supervised learning models, such as decision trees and support vector machines (SVMs), have been utilized to perform imputation of missing data by predicting the most likely values based on the available information. Similarly, unsupervised learning methods, such as clustering algorithms, have been employed to group similar data points and remove outliers that could distort the performance of analytical models. Moreover, deep learning techniques, particularly autoencoders and generative adversarial networks (GANs), have demonstrated remarkable effectiveness in transforming high-dimensional medical data into lower-dimensional representations, enabling more efficient and accurate model training. In addition to the discussion of these algorithms, the paper emphasizes the role of natural language processing (NLP) in automating the preprocessing of unstructured healthcare data, such as clinical notes and diagnostic reports. NLP techniques, including named entity recognition (NER) and word embeddings, are instrumental in extracting relevant information from unstructured text, standardizing terminologies, and converting textual data into structured formats suitable for downstream analysis. Furthermore, AI-based feature selection algorithms are explored, which aim to identify the most relevant features in the dataset, thereby reducing its dimensionality and improving the computational efficiency of predictive models. The study goes on to highlight the significant reduction in processing time achieved by AIdriven automation of preprocessing tasks. In conventional settings, data preprocessing accounts for a substantial portion of the time spent on building healthcare models, often requiring expert intervention to manually inspect and clean the data. By employing AI algorithms, not only can this process be expedited, but the accuracy of the resulting data is also enhanced, which translates into better model performance. The paper provides a detailed comparative analysis of manual preprocessing methods versus automated AI-driven approaches, demonstrating the substantial time savings and improvements in data quality brought about by automation. (shrink)

he health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. (...) The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. (...) The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. A. (shrink)

The article explores the problem of preferable technological changes in the context of work. To this end, it addresses the ‘why’ (motives and values) and the ‘how’ (organizational forms) of automation from a normative perspective. Concerning the ‘why,’ automation processes are currently mostly driven by values of economic efficiency. Yet, since automation processes are part of the basic structure of society, as is the division of labor, considerations of justice apply to them. As for the ‘how,’ the article (...) suggests ‘fair hybrid cooperation’ as an alternative ideal to guide automation priorities. It is achieved when the division of labor between humans and machines does not hinder, and preferably enables, workers’ experience of certain primary qualities in their work activity. By analogy with Rawls’ primary goods, these are qualities that it is necessary for every worker to experience in order to pursue their life plans: security, self-direction, self-development, dignity, and recognition. According to this criterion, the tasks to automate first are those hindering these contributive primary qualities. This provides us with a normative vocabulary and a standard to orient automation decisions but also to assess existing organizational forms and envisage alternative cooperative imaginaries. To illustrate how the ideal may play out in the real world, the article concludes with a ‘fair hybrid cooperation test’ and a nurse bots case for meaningful work. (shrink)

In today's digital era, large enterprises face the daunting task of managing and analyzing vast volumes of financial data to inform strategic decision-making and maintain a competitive edge. Traditional data warehousing solutions often fall short in addressing the scale, complexity, and performance demands of modern financial analytics. This paper explores the architectural principles, technological strategies, and best practices essential for building scalable data warehouses tailored to the needs of financial analytics in large organizations. It delves into data integration techniques, performance (...) optimization methods, security measures, and compliance with regulatory standards. Through in-depth analysis and real-world case studies, the paper provides a comprehensive roadmap for practitioners aiming to design and implement robust, scalable, and secure data warehousing solutions. (shrink)

This article will rework the classical question ‘Can a machine think?’ into a more specific problem: ‘Can a machine think anything new?’ It will consider traditional computational tasks such as prediction and decision-making, so as to investigate whether the instrumentality of these operations can be understood in terms of the creation of novel thought. By addressing philosophical and technoscientific attempts to mechanise thought on the one hand, and the philosophical and cultural critique of these attempts on the other, I will (...) argue that computation’s epistemic productions should be assessed vis-à-vis the logico-mathematical specificity of formal axiomatic systems. Such an assessment requires us to conceive automated modes of thought in such a way as to supersede the hope that machines might replicate human cognitive faculties, and to thereby acknowledge a form of onto-epistemological autonomy in automated ‘thinking’ processes. This involves moving beyond the view that machines might merely simulate humans. Machine thought should be seen as dramatically alien to human thought, and to the dimension of lived experience upon which the latter is predicated. Having stepped outside the simulative paradigm, the question ‘Can a machine think anything new?’ can then be reformulated. One should ask whether novel behaviour in computing might come not from the breaking of mechanical rules, but from following them: from doing what computers do already, and not what we might think they should be doing if we wanted them to imitate us. (shrink)

ABSTRACT: So far in this book, we have examined algorithmic decision systems from three autonomy-based perspectives: in terms of what we owe autonomous agents (chapters 3 and 4), in terms of the conditions required for people to act autonomously (chapters 5 and 6), and in terms of the responsibilities of agents (chapter 7). -/- In this chapter we turn to the ways in which autonomy underwrites democratic governance. Political authority, which is to say the ability of a government to (...) exercise power, may be justifiable or not. Whether it is justified and how it can come to be justified is a question of political legitimacy. Political legitimacy is another way in which autonomy and responsibility are linked. This relationship is the basis of the current chapter, and it is important in understanding the moral salience of algorithmic systems. We will draw the connection as follows. We begin, in section 8.1, by describing two uses of technology: crime predicting technology used to drive policing practices and social media technology used to influence elections (including by Cambridge Analytica and by the Internet Research Agency). In section 8.2 we consider several views of legitimacy and argue for a hybrid version of normative legitimacy based on one recently offered by Fabienne Peter. In section 8.3 we will explain that the connection between political legitimacy and autonomy is that legitimacy is grounded in legitimating processes, which are in turn based on autonomy. Algorithmic systems—among them PredPol and the Cambridge Analytica-Facebook-Internet Research Agency amalgam—can hinder that legitimation process and conflict with democratic legitimacy, as we argue in section 8.4. We will conclude by returning to several cases that serve as through-lines to the book: Loomis, Wagner, and Houston Schools. -/- The link below is to an open-access copy of the chapter. (shrink)

Technology has the power to break the limitations of traditional passive learning and innovate almost all aspects of everyday life with the power of connecting things of the world to the Internet, “Internet of Things (IoT).” IoT is no longer a phenomenon, but it has become a prevalent system in which people, processes, data, and things connect to the Internet and each other. This paper ‘Enhanced Campus Automation System and the emerging need of IoT integration: an automation perspective’ [eCAS-IoT] (...) is an outcome of case study undertaken at St. Aloysius’ College, Jabalpur, India, wherein the automation challenges are taken into consideration that has been integrated in the existing ERP and related tools such as RFID based attendance and monitoring system in real-time, digital library, API integration and implementation of Optical Network. In this paper we have tried to bring out ideas to achieve the campus automation challenges and awareness of greater need of technical expertise, promotion and training on the effective use of ICT and related devices to make teaching, learning and administration more impacting. (shrink)

This paper delves into the use of machine learning-based systems in decision-making processes and its implications for moral responsibility as traditionally defined. It focuses on the emergence of responsibility gaps and examines proposed strategies to address them. The paper aims to provide an introductory and comprehensive overview of the ongoing debate surrounding moral responsibility in automated decision-making. By thoroughly examining these issues, we seek to contribute to a deeper understanding of the implications of AI integration in society.

Using automated systems to avoid the need for human discretion in government contexts – a scenario we call ‘rule by automation’ – can help us achieve the ideal of a free and equal society. Drawing on relational theories of freedom and equality, we explain how rule by automation is a more complete realization of the rule of law and why thinkers in these traditions have strong reasons to support it. Relational theories are based on the absence of human domination (...) and hierarchy, which automation helps us achieve. Nevertheless, there is another understanding of relational theories where what matters is the presence of valuable relationships with those in power. Exploring this further might help us see when and why we should accept human discretion. (shrink)

Many of the benefits and burdens we might experience in our lives — from bank loans to bail terms — are increasingly decided by institutions relying on algorithms. In a sense, this is nothing new: algorithms — instructions whose steps can, in principle, be mechanically executed to solve a decision problem — are at least as old as allocative social institutions themselves. Algorithms, after all, help decision-makers to navigate the complexity and variation of whatever domains they are designed for. In (...) another sense, however, this development is startlingly new: not only are algorithms being deployed in ever more social contexts, they are being mechanically executed not merely in principle, but pervasively in practice. Due to recent advances in computing technology, the benefits and burdens we experience in our lives are now increasingly decided by automata, rather than each other. How are we to morally assess these technologies? In this chapter, I propose a preliminary conceptual schema for identifying and locating the various injustices of automated, algorithmic social decision systems, from a broadly liberal perspective. (shrink)

The fields of Responsible Research and Innovation (RRI) and participatory foresight seek to establish, and to include publics within, anticipatory governance mechanisms. While scenario-based methods can bring to the publics’ attention the ethical challenges associated to existing technologies, there has been little empirical research examining how, in practice, prospective public deliberative processes should be organized to inform anticipatory governance. The goal of this article is to generate methodological insights into the way such methods can stimulate the public's (...) moral imagination regarding what may (or may not) happen in the future and what should (or should not) happen in the future. Our qualitative analyses draw on a public deliberation study that included videos and online scenarios to support participants’ (n = 57) deliberations about fictional interventions for genetically at-risk individuals. Our findings clarify how participants: (1) challenged key elements of our scenarios; (2) extended several of their technical and moral prospects; (3) engaged personally with others, including our scenarios’ characters; and (4) mobilized the past creatively to reason about the future. Our methodology enabled participants to creatively and empathetically envision complex sociotechnical futures. Yet, important methodological limits should be acknowledged by those who design, implement and use public engagement methods to inform anticipatory governance. (shrink)

In this paper I critique the ethical implications of automating CCTV surveillance. I consider three modes of CCTV with respect to automation: manual, fully automated, and partially automated. In each of these I examine concerns posed by processing capacity, prejudice towards and profiling of surveilled subjects, and false positives and false negatives. While it might seem as if fully automated surveillance is an improvement over the manual alternative in these areas, I demonstrate that this is not necessarily (...) the case. In preference to the extremes I argue in favour of partial automation in which the system integrates a human CCTV operator with some level of automation. To assess the degree to which such a system should be automated I draw on the further issues of privacy and distance. Here I argue that the privacy of the surveilled subject can benefit from automation, while the distance between the surveilled subject and the CCTV operator introduced by automation can have both positive and negative effects. I conclude that in at least the majority of cases more automation is preferable to less within a partially automated system where this does not impinge on efficacy. (shrink)

Pneumonia is a serious respiratory infection that poses significant health risks, particularly if not diagnosed and treated promptly. Traditional methods of pneumonia diagnosis rely on the manual interpretation of chest X-ray images by radiologists, a process that can be time-consuming, subjective, and error-prone, especially in regions with limited access to experienced medical professionals. To address these challenges, this study explores the development of an automated deep learning-based system for pneumonia detection using chest X-ray images. The results demonstrate that the (...) deep learning model can achieve high levels of accuracy, sensitivity, and specificity, making it a valuable tool for assisting radiologists in diagnosing pneumonia more quickly and reliably. Moreover, the system's scalability and ease of deployment make it particularly beneficial in resource-limited settings, where timely and accurate diagnosis is crucial. This research highlights the potential of deep learning to revolutionize medical diagnostics, improving patient outcomes through enhanced diagnostic accuracy and efficiency. (shrink)

Responsible research and innovation imposes normative requirements on research and innovation processes resembling three successive steps, each more ambitious than its predecessor, with distinct features. For the research dimension the distinct features reflect the normative requirements of, first, credible research ; second, responsive research ; and third, responsible research. Equally distinct features reflect the requirements of credible innovation, responsive innovation, and responsible innovation.

Autonomous AI agents present transformative opportunities and significant governance challenges. Existing frameworks, such as the EU AI Act and the NIST AI Risk Management Framework, fall short of addressing the complexities of these agents, which are capable of independent decision-making, learning, and adaptation. To bridge these gaps, we propose the ETHOS (Ethical Technology and Holistic Oversight System) framework—a decentralized governance (DeGov) model leveraging Web3 technologies, including blockchain, smart contracts, and decentralized autonomous organizations (DAOs). ETHOS establishes a global registry (...) for AI agents, enabling dynamic risk classification, proportional oversight, and automated compliance monitoring through tools like soulbound tokens and zero-knowledge proofs. Furthermore, the framework incorporates decentralized justice systems for transparent dispute resolution and introduces AI-specific legal entities to manage limited liability, supported by mandatory insurance to ensure financial accountability and incentivize ethical design. By integrating philosophical principles of rationality, ethical grounding, and goal alignment, ETHOS aims to create a robust research agenda for promoting trust, transparency, and participatory governance. This innovative framework offers a scalable and inclusive strategy for regulating AI agents, balancing innovation with ethical responsibility to meet the demands of an AI-driven future. (shrink)

Governance encompasses the processes by which organizations are directed, controlled and held to account. It includes the authority, accountability, leadership, direction, and control exercised in an organization. Greatness can be achieved when good governance principles and practices are applied throughout the whole organization. Ethical Governance requires that public officials adhere to high moral standards while serving others. Authentic Governance entails the systematic process of continuous, gradual, and routine personal/corporate improvement, steering, and learning that lead to (...) sustainable high personal/corporate performance and excellence. Thus authentic governance represents the ability to discern right from wrong and the commitment to do what is right, good, and proper. Benevolent governance is perceived to be for benefit of the population as a whole. Sikhism, a panentheistic religion, originated in the Punjab province of the Indian subcontinent, during the 15th century. It is one of the youngest and fifth major world religions, founded by Guru Nanak. The fundamental beliefs of Sikhism have been enshrined in the sacred scripture, Sri Guru Granth Sahib [1-3]. These beliefs include faith in one universal creator, unity of all humankind, engaging in selfless service, striving for social justice for all, honest livelihood and ethical conduct while living a householder's life. Sri Guru Grant Sahib provides very useful insights for governance through religious or secular organizations, that address human rights, education, or diplomacy to end all forms of disparity/discrimination among people [4-5]. In this article, an attempt is made to understand how the doctrines of Sikhism encourage us for better governance and by which means. Whether these doctrines align with the above-mentioned forms of governance or conflict with these. It is pointed out the most of the attributes of good, ethical, authentic and benevolent governance are genuinely supported by the Sikh doctrines. The adherence to democratic and republican organizations, for providing high-quality governance, is encouraged in Sikh principles and practices. (shrink)

In the biomedical context, policy makers face a large amount of potentially discordant evidence from different sources. This prompts the question of how this evidence should be aggregated in the interests of best-informed policy recommendations. The starting point of our discussion is Hunter and Williams’ recent work on an automated aggregation method for medical evidence. Our negative claim is that it is far from clear what the relevant criteria for evaluating an evidence aggregator of this sort are. What is (...) the appropriate balance between explicitly coded algorithms and implicit reasoning involved, for instance, in the packaging of input evidence? In short: What is the optimal degree of ‘automation’? On the positive side: We propose the ability to perform an adequate robustness analysis as the focal criterion, primarily because it directs efforts to what is most important, namely, the structure of the algorithm and the appropriate extent of automation. Moreover, where there are resource constraints on the aggregation process, one must also consider what balance between volume of evidence and accuracy in the treatment of individual evidence best facilitates inference. There is no prerogative to aggregate the total evidence available if this would in fact reduce overall accuracy. (shrink)

The era of AI-based decision-making fast approaches, and anxiety is mounting about when, and why, we should keep “humans in the loop” (“HITL”). Thus far, commentary has focused primarily on two questions: whether, and when, keeping humans involved will improve the results of decision-making (making them safer or more accurate), and whether, and when, non-accuracy-related values—legitimacy, dignity, and so forth—are vindicated by the inclusion of humans in decision-making. Here, we take up a related but distinct question, which has eluded the (...) scholarship thus far: does it matter if humans appear to be in the loop of decision-making, independent from whether they actually are? In other words, what is stake in the disjunction between whether humans in fact have ultimate authority over decision-making versus whether humans merely seem, from the outside, to have such authority? Our argument proceeds in four parts. First, we build our formal model, enriching the HITL question to include not only whether humans are actually in the loop of decision-making, but also whether they appear to be so. Second, we describe situations in which the actuality and appearance of HITL align: those that seem to involve human judgment and actually do, and those that seem automated and actually are. Third, we explore instances of misalignment: situations in which systems that seem to involve human judgment actually do not, and situations in which systems that hold themselves out as automated actually rely on humans operating “behind the curtain.” Fourth, we examine the normative issues that result from HITL misalignment, arguing that it challenges individual decision-making about automated systems and complicates collective governance of automation. (shrink)

The rise of social media has created a new platform for communication and interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying. Detecting and mitigating cyberbullying on social media platforms is a critical challenge that requires advanced technological solutions. This paper presents a novel approach to cyberbullying detection using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques, enhanced by optimization algorithms. The proposed system is designed to identify and classify cyberbullying (...) behavior in real-time, analyzing textual data from social media posts to detect harmful content. The model is trained on a large dataset of labeled instances of bullying and non-bullying content, using supervised ML algorithms such as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques, including sentiment analysis, keyword extraction, and text vectorization, are employed to preprocess and transform the data into a format suitable for machine learning. To optimize the performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved accuracy and reduced false positives. The system's effectiveness is validated through experiments conducted on various social media platforms, demonstrating its potential to detect cyberbullying with high precision. Future work will focus on enhancing the model's adaptability to emerging slang and evolving language patterns in social media. Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural Language Processing (NLP), Optimization Techniques. (shrink)