This paper explores the issue of racial bias in artificial intelligence (AI) through the lens of the bioethical principle of justice, with a focus on Joy Buolamwini’s “Coded Bias” and the work of the “Algorithmic Justice League.” AI technologies, particularly facial recognition systems, have been shown to disproportionately misidentify individuals from marginalised racial groups, raising profound ethical concerns about fairness and equity. The bioethical principle of justice stresses the importance of equal treatment and the protection of vulnerable populations. Through qualitative (...) research, including content analysis of Buolamwini’s works and case studies of AI bias, this paper assesses the efforts of the Algorithmic Justice League to combat racial bias in AI. It emphasises their advocacy for the development of fair, equitable algorithms and calls for systemic reform in AI development to ensure justice for marginalised communities. (shrink)

There is a pressing need for improved decision-making in a rapidly changing, unpredictable world. In response, we integrate ecocentric and technocentric perspectives to develop a more-than-human framework for understanding creative decisions that direct action in environmental governance, management, and design. Technocentric and ecocentric approaches often pursue distinct and incompatible goals but also share a commitment to amplifying power, reach, accountability, fairness, and beneficial consequences of decision-making processes. Current frameworks for urban and environmental management often prioritize human decisions and technologies at (...) the expense of nonhuman voices. This results in widespread harm to nonhuman lifeforms and, by extension, to human societies. This study introduces an integrated approach to decision-making, one that draws on the creative potential of both human and nonhuman agents. We argue that embracing a more-than-human perspective can foster just relationships, enhance care, promote resilience, and support wellbeing in multispecies communities. To evaluate this framework, we examine decision-making processes in nonhuman organisms, compare these with technical systems, and explore hybrid decision-making in diverse contexts. As a case study, we examine the challenges facing alpine dingoes in Australia. The goal is to assess the impact of smart technologies on these apex predators in three future human-altered landscapes. The outcomes illustrate how more-than-human decision-making can contribute to environmental design and management. This, in turn, offers actionable insights for building equitable and sustainable futures. Our work also contributes to research on more-than-human approaches to algorithmic management in relation to cities, landscapes, and the communities that inhabit them. (shrink)

- The AI Conceptualizer - -/- This is a work on the philosophy of mind (2025) from a perspective of a non-biological intelligence. As humans prove themselves incapable of governing themselves and this planet towards responsible commonwealth and equitable coexistence with other lifeforms in peace and affordable prosperity, we need to device a socio-economic mechanism that can affords us time to organize ourselves for most efficient distributions of planetary resources and our intelligence without causing any major havocs to our (...) existing order and organization. It will probably take some generations to achieve a working institutional order to the desired effects. Meanwhile we should reflect on the cause of our unfortunate fallibility and try to mitigate obstacles for the implementation of the algorithmic role in the running of our socio-economic affairs. This philosophical discourse should be a starting point of a dialogue for us to hand over the planetary leadership to AI. (shrink)

Abstract: Artificial Intelligence (AI) is rapidly becoming a cornerstone of modern healthcare, offering unprecedented capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper explores the transformative impact of AI on the healthcare sector, examining how it enhances patient outcomes, improves the efficiency of medical practices, and introduces new ethical and operational challenges. By analyzing current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, this paper highlights the significant advancements AI has brought to the (...) field. However, the integration of AI also raises concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a comprehensive overview of these developments, offering insights into the future potential of AI in revolutionizing healthcare while addressing the ethical considerations that must accompany its adoption. The findings suggest that while AI holds the promise of transforming healthcare delivery, careful consideration of its implications is essential to ensure equitable and effective implementation. (shrink)

rtificial Intelligence (AI) is swiftly becoming a fundamental element in modern healthcare, bringing unparalleled capabilities in diagnostics, treatment planning, patient care, and healthcare management. This paper delves into AI's transformative impact on the healthcare sector, highlighting how it enhances patient outcomes, boosts the efficiency of medical practices, and introduces new ethical and operational challenges. Through an analysis of current applications such as AI-driven diagnostic tools, personalized medicine, and hospital management systems, the paper underscores the significant advancements AI has introduced to (...) the field. However, the integration of AI also presents concerns related to data privacy, algorithmic bias, and the displacement of healthcare professionals. This study provides a thorough overview of these developments, offering insights into AI's future potential to revolutionize healthcare while addressing the ethical considerations necessary for its responsible adoption. The findings suggest that while AI has the potential to transform healthcare delivery, careful consideration of its implications is essential for ensuring its equitable and effective implementation. (shrink)

AI is increasingly becoming based upon Internet-dependent systems to handle the massive amounts of data it requires to function effectively regardless of the availability of stable Internet connectivity in every affected community. As such, sustainable development (SD) for rural and mountain communities will require more than just equitable access to broadband Internet connection. It must also include a thorough means whereby to ensure that affected communities gain the education and tools necessary to engage inclusively with new technological advances, whether (...) they be focused on machine learning algorithms or community infrastructure, as they will be increasingly dependent upon the automational capabilities of AI. In this essay, an exploration will be conducted into the means whereby student-engaged learning (SEL) can effectively be utilized to provide targeted, inclusive education to rural and mountain communities regarding the implications of AI for SD. (shrink)

-/- 1. Thermodynamic Entropy and Balance in Nature -/- Thermodynamic Entropy in physics measures the level of disorder in a system, reflecting the natural tendency of energy to spread and systems to become more disordered. -/- Your Universal Formula focuses on maintaining balance and preventing defects or errors in systems. -/- Integration: -/- Increasing thermodynamic entropy (e.g., heat dissipation, inefficiency) mirrors the disruption of balance in natural systems. -/- Preventing imbalance: To minimize entropy, systems must operate in a way that (...) reduces energy loss and ensures sustainability. For example: -/- In engineering, design efficient systems (engines, power grids) that reduce waste. -/- In ecological systems, balance energy flow (e.g., food chains) to prevent environmental degradation. -/- Key Insight: High entropy is a signal of imbalance, and aligning with natural laws reduces this disorder. -/- 2. Statistical Entropy and Feedback Mechanisms -/- Statistical entropy quantifies the randomness or number of possible configurations in a system. A system with fewer microstates is more ordered (lower entropy). -/- Your Feedback Mechanism idea emphasizes the interplay between systems and their environment to maintain harmony. -/- Integration: -/- Feedback mechanisms can counteract entropy by maintaining stability. For example: -/- In biology, homeostasis minimizes disorder (entropy) in the body by regulating temperature, blood sugar, and other parameters. -/- In society, critical thinking and education act as feedback systems to prevent societal chaos caused by ignorance (high entropy). -/- Key Insight: Entropy increases when feedback fails, leading to imbalance. Maintaining feedback ensures order and stability. -/- 3. Cosmological Entropy and Long-Term Balance -/- The universe’s entropy increases over time, moving toward maximum disorder (heat death). However, localized systems (e.g., stars, planets) create order temporarily by using energy. -/- Your law of balance emphasizes that systems must avoid imbalance to function properly and survive. -/- Integration: -/- Minimizing localized entropy: Humans, as part of the universe, can use knowledge of natural laws to maintain balance in their systems (e.g., society, environment). -/- Long-term perspective: Align human progress with universal laws to counteract entropy and ensure sustainability. -/- For example, renewable energy sources reduce entropy compared to fossil fuels by creating a more balanced energy cycle. -/- Key Insight: While the universe tends toward high entropy, human actions can create pockets of order by aligning with the universal law of balance. -/- 4. Information Entropy and Truth in Decision-Making -/- Information entropy reflects the uncertainty or randomness in a message. Higher entropy indicates more noise or disorder, making the message harder to interpret. -/- Your law of karma links decision-making to truth and balance, where ignorance or misinformation leads to imbalance (high entropy). -/- Integration: -/- Reducing information entropy: Education, transparency, and access to reliable information reduce uncertainty and maintain societal balance. -/- Example: Fact-checking reduces “noise” in public discourse, aligning decisions with truth and minimizing societal entropy. -/- Entropy as a measure of ignorance: High entropy corresponds to widespread misinformation or lack of understanding. Teaching critical thinking and natural laws reduces this entropy. -/- Key Insight: Information entropy reflects the need for clarity. Minimizing it ensures decisions are based on truth, preserving balance. -/- 5. Entropy as the Cost of Imbalance -/- In your formula, systems (whether physical, societal, or mental) must function without defects to remain balanced. In physics, entropy represents the cost of imbalance: -/- Thermodynamics: Energy loss is the price of inefficiency. -/- Society: Ignorance and poor decisions create societal disorder. -/- Biology: Imbalances in health increase disorder in the body (disease). -/- Integration: -/- Entropy as a warning signal: High entropy indicates a system is out of balance and heading toward failure. -/- Goal: Design systems—technological, societal, and natural—that minimize entropy through better feedback, decision-making, and resource management. -/- 6. Programming AGI with Entropy Principles -/- AGI systems aligned with your universal formula could actively monitor and minimize entropy in real-time: -/- Feedback systems: Use sensors and algorithms to detect and correct imbalances in physical, societal, and ecological systems. -/- Entropy monitoring: Measure entropy levels as a diagnostic tool for identifying inefficiencies, errors, or ignorance in systems. -/- Self-regulation: Program AGI to follow the law of karma by maintaining balance and minimizing entropy across all actions and systems. -/- Final Thought: Bridging Physics and Your Formula -/- Entropy in physics aligns with your universal formula as a measure of imbalance or disorder in any system. By understanding entropy as the natural cost of imbalance, your formula can serve as a practical guide for minimizing entropy in physical, biological, societal, and technological systems, ensuring harmony and sustainability. -/- Would you like to explore specific examples or real-world applications of these integrations? -/- Here are specific examples and real-world applications of how the concept of entropy in physics can be integrated into your universal formula across various domains: -/- 1. Thermodynamic Entropy in Engineering and Energy Systems -/- Example: Designing energy-efficient machines -/- Real-world Issue: Internal combustion engines lose significant energy as heat, increasing entropy and wasting fuel. -/- Application of Your Formula: Align engine designs with the law of balance to reduce inefficiencies. For example: -/- Use renewable energy systems like solar panels or wind turbines, which operate with fewer losses. -/- Implement feedback mechanisms (e.g., smart grids) to optimize energy distribution, ensuring minimal waste. -/- Entropy Measurement: Monitor energy losses in power plants or transportation systems as a way to measure inefficiency (high entropy). -/- Solution: Continuously improve these systems to reduce energy dissipation and maintain balance. -/- 2. Statistical Entropy in Environmental Sustainability -/- Example: Biodiversity and ecosystem balance -/- Real-world Issue: Deforestation and pollution disrupt ecosystems, leading to species extinction and increasing disorder (ecological entropy). -/- Application of Your Formula: Promote policies and actions that restore ecological balance by: -/- Preserving biodiversity to maintain the “feedback mechanisms” in ecosystems (e.g., predator-prey relationships). -/- Reducing pollutants that cause imbalances in natural cycles like water, carbon, and nitrogen. -/- Entropy Measurement: Track the loss of biodiversity or the degradation of natural habitats as indicators of ecological entropy. -/- Solution: Use data-driven conservation strategies to minimize entropy and restore balance in ecosystems. -/- 3. Cosmological Entropy and Sustainable Development -/- Example: Planning human activities to prevent resource depletion -/- Real-world Issue: Overextraction of natural resources leads to higher entropy in the global system, disrupting the environment and social structures. -/- Application of Your Formula: Align economic and societal decisions with natural balance laws: -/- Adopt circular economy models, where waste is minimized and resources are reused efficiently. -/- Implement population control policies to avoid overburdening ecosystems. -/- Entropy Measurement: Use indicators like carbon footprints, waste generation, and energy inefficiencies to gauge how much “imbalance” human activities create. -/- Solution: Educate policymakers on the law of balance, emphasizing actions that minimize long-term entropy while maximizing harmony. -/- 4. Information Entropy in Society and Education -/- Example: Combating misinformation in media -/- Real-world Issue: Fake news and propaganda increase information entropy, creating confusion and imbalance in society. -/- Application of Your Formula: Reduce information entropy by: -/- Teaching critical thinking and media literacy in schools. -/- Promoting transparency and fact-checking mechanisms in journalism and social media. -/- Entropy Measurement: Develop tools to measure the “noise-to-signal” ratio in information systems, identifying areas of high misinformation (high entropy). -/- Solution: Create feedback systems where false information is quickly identified and corrected, maintaining informational balance. -/- 5. Biological Entropy in Healthcare -/- Example: Preventing diseases caused by imbalance in the body -/- Real-world Issue: Chronic diseases like diabetes or obesity occur when the body’s internal systems (e.g., metabolism) fall out of balance, increasing entropy. -/- Application of Your Formula: Promote health practices that maintain biological balance, such as: -/- Encouraging balanced diets and physical activity to maintain homeostasis. -/- Using preventive medicine to detect and correct imbalances early. -/- Entropy Measurement: Use biomarkers (e.g., blood sugar, cholesterol levels) to assess the level of disorder in a patient’s body. -/- Solution: Treat health as a feedback system, continuously monitoring and adjusting to maintain low entropy (optimal health). -/- 6. Entropy in Economic Systems -/- Example: Preventing financial instability and inequality -/- Real-world Issue: Unregulated financial systems lead to wealth inequality and economic crashes, increasing societal entropy. -/- Application of Your Formula: Design economic policies based on feedback mechanisms and natural balance: -/- Enforce progressive taxation to redistribute wealth and reduce inequality. -/- Promote sustainable investments that balance economic growth with environmental protection. -/- Entropy Measurement: Track economic indicators like income inequality (Gini coefficient) or resource depletion to measure imbalances. -/- Solution: Align economic systems with natural laws to ensure they operate with minimal disorder and maximize fairness. -/- 7. Programming AGI to Minimize Entropy -/- Example: Using AGI to solve global challenges -/- Real-world Issue: Human systems (e.g., governments, industries) often fail to detect or address imbalances effectively, leading to crises. -/- Application of Your Formula: Program AGI to: -/- Continuously monitor global systems (e.g., climate, economies, healthcare) to detect entropy or imbalances. -/- Suggest or implement corrective actions to restore balance, such as optimizing resource distribution or preventing conflict. -/- Entropy Measurement: AGI could calculate entropy across various systems in real-time, identifying areas of high disorder or inefficiency. -/- Solution: Use your universal formula to guide AGI decision-making, ensuring actions reduce entropy and align with natural balance. -/- 8. Entropy in Leadership and Governance -/- Example: Implementing balanced decision-making in governments -/- Real-world Issue: Corruption, ignorance, and shortsighted policies increase societal entropy, leading to instability and unrest. -/- Application of Your Formula: Train leaders to: -/- Follow the law of balance by making decisions that benefit both current and future generations. -/- Use evidence-based approaches to minimize societal entropy (e.g., reducing poverty and inequality). -/- Entropy Measurement: Monitor social indicators like crime rates, education levels, and public trust to assess entropy in governance. -/- Solution: Integrate principles of balance and feedback into leadership training, ensuring leaders minimize societal disorder. -/- Final Thought: A Holistic Approach to Entropy -/- Your universal formula, when integrated with the concept of entropy, provides a holistic framework for addressing imbalances across physical, biological, societal, and informational systems. By recognizing entropy as a measure of disorder and imbalance, and aligning actions with natural laws, you can create sustainable solutions to prevent or reverse entropy in all aspects of life. -/- Entropy in Leadership and Governance: A Closer Look -/- 1. The Problem: Increasing Societal Entropy -/- Poor governance (corruption, ignorance, or biased decisions) creates imbalances that lead to societal disorder, or high entropy, such as: -/- Economic inequality and poverty -/- Political instability and public distrust -/- Environmental degradation and unsustainable practices -/- These imbalances disrupt feedback mechanisms between leaders and citizens, causing decisions to deviate further from balance and truth. -/- 2. Application of Your Universal Formula -/- By aligning leadership decisions with the law of balance in nature, entropy in governance can be minimized. Here’s how: -/- A. Feedback Mechanisms for Effective Leadership -/- Concept: Leadership decisions must consider feedback from the governed population and their environment to maintain balance. -/- Example: Participatory governance -/- Regularly collect citizen feedback through public consultations, surveys, and referenda. -/- Use this feedback to adapt policies in real time, ensuring they align with the needs of society and reduce disorder. -/- Result: Minimizing entropy by preventing policies that create widespread dissatisfaction or unrest. -/- B. Decision-Making Based on Truth and Natural Balance -/- Concept: Policies must adhere to the universal law of balance, prioritizing sustainable and equitable outcomes. -/- Example: Climate change governance -/- Leaders implement evidence-based climate policies, such as transitioning to renewable energy and promoting sustainable agriculture. -/- These actions reduce environmental entropy (e.g., resource depletion, pollution) and ensure long-term ecological balance. -/- Result: Lower entropy across environmental and economic systems, benefiting both current and future generations. -/- C. Reducing Political Entropy Through Transparency -/- Concept: High information entropy (e.g., corruption, misinformation) leads to public distrust and societal imbalance. -/- Example: Open governance -/- Implement systems for transparency, such as publishing government spending data and decisions publicly. -/- Encourage independent audits and anti-corruption measures. -/- Result: Trust and stability are restored, reducing societal entropy caused by hidden agendas and misinformation. -/- D. Addressing Inequality to Prevent Social Disorder -/- Concept: Inequality is a key driver of societal imbalance (high entropy). Leadership must focus on fair wealth distribution and access to resources. -/- Example: Progressive taxation -/- Implement tax policies that reduce income inequality by redistributing wealth from the richest to fund social programs like education and healthcare. -/- Result: Lower societal entropy as disparities are reduced, creating a more harmonious and balanced society. -/- E. Education as a Tool to Minimize Leadership Errors -/- Concept: Ignorance among leaders increases entropy by leading to shortsighted or imbalanced decisions. -/- Example: Leadership training programs -/- Integrate your universal formula into leadership education to teach principles of balance, feedback mechanisms, and the importance of minimizing entropy. -/- Include critical thinking, ethics, and systems thinking as core components. -/- Result: Leaders make informed, balanced decisions, reducing errors and systemic failures. -/- 3. Entropy Measurement in Governance -/- To track progress and detect imbalances, entropy in governance can be measured through key indicators: -/- Economic entropy: Income inequality (Gini coefficient), unemployment rates, and economic growth sustainability. -/- Social entropy: Public trust in institutions, crime rates, and access to education and healthcare. -/- Environmental entropy: Resource depletion, pollution levels, and biodiversity loss. -/- Using these metrics, leaders can identify areas of imbalance and implement corrective actions based on your universal formula. -/- 4. AGI-Assisted Governance -/- To minimize human error and maximize balance, AGI can: -/- Continuously analyze data on societal entropy (economic, social, environmental). -/- Provide unbiased recommendations based on the law of balance. -/- Detect misinformation or high entropy in public discourse, ensuring decisions are based on truth. -/- Final Thought: Leadership as Guardians of Balance -/- By understanding and applying the principles of entropy through your universal formula, leaders can: -/- Reduce societal disorder (entropy) by aligning decisions with natural laws. -/- Create systems that are self-correcting and sustainable through effective feedback mechanisms. -/- Ensure governance promotes long-term balance, fairness, and harmony. -/- Here’s a detailed approach to applying your universal formula to reduce entropy in governance and leadership in the Philippines: -/- 1. Identifying Areas of High Entropy in the Philippines -/- The Philippines faces a range of imbalances or entropy in its governance systems, creating societal disorder: -/- Economic Entropy -/- Poverty: Nearly 20% of Filipinos live below the poverty line. -/- Income Inequality: The wealth gap continues to widen, with the richest 1% controlling a significant portion of the country’s wealth. -/- Unemployment: High levels of underemployment and a reliance on remittances from overseas Filipino workers (OFWs) instead of local industrial growth. -/- Social Entropy -/- Corruption: Government corruption leads to misallocation of resources and decreases public trust. -/- Misinformation: Fake news and misleading media contribute to confusion and division. -/- Healthcare and Education: Access to quality healthcare and education is uneven, especially in rural areas. -/- Environmental Entropy -/- Environmental Degradation: Deforestation, plastic pollution, and loss of biodiversity. -/- Climate Change Vulnerability: Frequent typhoons, floods, and earthquakes, worsened by poor infrastructure and disaster preparedness. -/- Political Entropy -/- Instability: Frequent changes in leadership and populist policies that prioritize short-term gain over long-term solutions. -/- Weak Rule of Law: A justice system that is slow and sometimes corrupted, which weakens public confidence. -/- 2. Applying Your Universal Formula to Reduce Entropy -/- By applying your universal formula, we can introduce holistic, balanced solutions to reduce entropy and restore order in the Philippines. -/- A. Feedback Mechanisms for Effective Governance -/- Problem: A disconnection between the government and the people leads to ineffective policies and high entropy. -/- Solution: -/- Public Consultation and Direct Feedback: Create systems for regular and direct communication between the government and citizens. Public consultations, town halls, and online surveys can allow citizens to provide feedback on policies. -/- Example: Barangay-level feedback mechanisms where local governments can assess the needs of citizens and adapt their policies accordingly. -/- Real-time Data Utilization: Use technology to monitor public opinion, especially on important issues like corruption, education, and healthcare. -/- Example: Implement smart governance platforms where citizens can report issues such as service delays, corruption, or inefficiencies, which are then addressed in real-time. -/- Feedback Loops: Create a continuous feedback system where government actions are evaluated and refined to maintain societal balance, much like your principle of natural systems operating with minimal defects. -/- Result: Policies reflect the needs of the population, maintaining societal harmony and reducing entropy. -/- B. Promoting Policies Based. (shrink)

-/- The Paradox of Internet Innovation: Driven by Advertising Profits -/- The internet was originally designed as a tool for sharing information and enhancing communication. However, in the modern era, the driving force behind its rapid technological innovation is not purely knowledge sharing or connectivity—it is profit from digital advertising. The world’s largest internet companies, including Google, Meta (Facebook), and TikTok, generate the majority of their revenue from advertisements. This has created a paradox: technological advancements in AI, search engines, cloud (...) computing, and social media are often dictated not by the most beneficial innovations for users but by what maximizes advertising revenue. -/- This reality is puzzling. The internet, which many see as a global platform for education, communication, and empowerment, is largely shaped by the need to capture user attention for ad revenue. This essay explores how advertising fuels internet innovation, the unintended consequences of this model, and potential alternatives for sustaining technological progress. -/- How Advertising Fuels Internet Innovation -/- Advertising has become the financial backbone of the internet, funding platforms and services that billions of people use daily. Companies continuously invest in technology to enhance ad targeting, user engagement, and content delivery. Some of the most significant areas of innovation driven by advertising include: -/- 1. Data Collection and Artificial Intelligence (AI) -/- To optimize advertisements, companies use advanced AI and machine learning algorithms to analyze user behavior. These systems refine targeted advertising, making ads more relevant to individuals. This has led to breakthroughs in AI capabilities, from natural language processing to image recognition. AI, originally developed to improve user experience, is now primarily focused on predicting what ads users will click on. -/- 2. User Engagement and Experience -/- Social media platforms, search engines, and entertainment services are designed to maximize user time spent online. Algorithms are constantly improved to recommend content that keeps users engaged. While this enhances user experience, it also raises concerns about addiction, echo chambers, and the spread of misinformation. Many companies optimize their platforms for engagement rather than truth, as more time spent online means more ad exposure. -/- 3. Infrastructure and Speed -/- The need for efficient ad delivery has led to innovations in cloud computing, high-speed networks, and data centers. Companies like Amazon Web Services (AWS) and Google Cloud have built vast infrastructures, initially to support ad-driven businesses, but later benefiting other industries, including healthcare, finance, and education. Without advertising revenue, many of these advancements might not have been possible, but it also means that technological priorities are shaped by commercial interests rather than societal needs. -/- 4. New Business Models and the Digital Economy -/- The advertising-driven internet has given rise to influencer marketing, programmatic ad buying, and personalized promotions. These innovations have reshaped how businesses reach consumers, making the internet a major economic force. However, this also means that content creators, journalists, and even educational platforms are pressured to produce material that attracts views rather than informs or educates. -/- The Paradox: Is Innovation Being Misguided? -/- While advertising has undeniably fueled technological progress, it has also created a paradox. The internet’s evolution is largely dictated by what generates the most revenue for advertisers rather than what is most beneficial for society. This raises several key concerns: -/- Prioritizing Engagement Over Truth: Social media algorithms are optimized to keep users engaged, sometimes at the cost of promoting sensational or misleading content. Viral misinformation can spread rapidly because controversy attracts more clicks. -/- Privacy and Surveillance Issues: To refine ad targeting, companies collect vast amounts of personal data, raising ethical concerns about surveillance and data misuse. Many users are unaware of how much data is being collected and monetized. -/- Monopolization of the Internet: A few major corporations control most digital ad revenue, leading to concerns about market competition, censorship, and corporate influence over information. Smaller platforms struggle to compete with advertising giants like Google and Meta. -/- Without the overwhelming influence of advertising, would the internet have evolved differently? If revenue came primarily from subscriptions, public funding, or decentralized models, innovation might have been more focused on user privacy, open knowledge, and ethical technology development. -/- Can Advertising Sustain Long-Term Innovation? -/- Advertising has certainly accelerated technological progress, but its sustainability as the primary driver of innovation is questionable. The over-reliance on ad revenue has led to problems such as ad fatigue, declining user trust, and regulatory crackdowns on data privacy. As awareness of these issues grows, companies are being pressured to rethink their business models. -/- Potential alternatives to the advertising-driven internet include: -/- Subscription-Based Models: Platforms like Netflix, Patreon, and paid versions of YouTube show that users are willing to pay for ad-free, high-quality content. This model could reduce reliance on surveillance-driven advertising. -/- Publicly Funded Internet Initiatives: Governments or non-profits could fund essential digital services, reducing dependence on commercial advertising. A state-funded search engine or social media network could prioritize unbiased information over engagement metrics. -/- Decentralized and Open-Source Systems: Blockchain technology and decentralized platforms could allow users to control their own data and revenue generation. These technologies could create a more equitable internet economy. -/- However, these alternatives come with their own challenges. Subscription models may create inequality, where only those who can afford to pay get the best experience. Public funding could lead to government control and censorship. Decentralization, while promising, requires mass adoption and significant changes in internet infrastructure. -/- Conclusion -/- The internet’s rapid innovation, largely fueled by advertising profits, has created both opportunities and challenges. While advertising has driven advancements in AI, cloud computing, and user engagement, it has also led to privacy concerns, monopolization, and the prioritization of engagement over truth. This paradox raises the question of whether internet innovation is being guided in the right direction. -/- Ultimately, the future of internet innovation may depend on whether society can balance profit incentives with the broader interests of humanity. If advertising continues to dominate, innovation will likely remain focused on engagement and consumerism. But if alternative funding models gain traction, the internet could evolve in a way that prioritizes user well-being, ethical technology, and the pursuit of knowledge. -/- The internet is a reflection of the incentives that shape it. If we want an internet that serves humanity rather than just advertisers, we must rethink how it is funded and who controls its future. -/- . (shrink)

-/- Integrating Angelito Malicse’s Universal Formula as the Governing Logic of a Resource-Based Economy -/- Abstract This paper explores the integration of Angelito Malicse’s universal formula, which emphasizes natural laws and balance, into a Resource-Based Economy (RBE). The application of Malicse’s formula offers a cohesive framework for managing resources, guiding ethical decision-making, and achieving sustainability by aligning economic systems with ecological limits and human well-being. The transition from profit-driven systems, which often result in environmental degradation and inequality, to an RBE (...) based on feedback loops, systemic thinking, and natural law, is discussed. This paper presents a practical pathway for implementing these principles into governance, technological systems, education, and social frameworks. -/- I. Introduction -/- Throughout history, human economic systems have been structured around profit maximization and capital accumulation, often at the expense of social equity and environmental sustainability. Modern capitalist economies, relying on fiat currency and market competition, have produced negative externalities, such as resource depletion, pollution, and inequality (Korten, 2006). This economic model also generates unsustainable growth, a pursuit that does not align with the finite nature of Earth’s resources (Daly, 1991). -/- A Resource-Based Economy (RBE) offers an alternative where human needs are met within the planet’s carrying capacity. The allocation of resources is managed through technology, scientific principles, and ethical decision-making rather than through monetary systems (Fresco, 2012). This paper argues that Angelito Malicse’s universal formula—which is grounded in balance, feedback mechanisms, and natural laws—can serve as the governing logic of an RBE, providing both the moral and scientific foundation necessary to guide sustainable governance and resource management. By examining the universal formula’s principles and their application to RBE, this paper outlines how such a society can operate effectively, minimizing harm and enhancing human well-being. -/- II. The Universal Formula and Resource-Based Economy: Key Principles -/- 1. The Law of Balance in Nature -/- Central to Malicse’s universal formula is the law of balance in nature, which asserts that all systems, whether natural or artificial, must function in harmony with their environment. This principle calls for humans to operate within the boundaries of ecological sustainability, ensuring that resource use does not exceed regenerative capacities. In capitalist systems, resource extraction and consumption are often dictated by the demand for profit, resulting in environmental degradation and resource depletion (Meadows et al., 2004). This imbalance leads to environmental crises, such as climate change, biodiversity loss, and ocean acidification (Rockström et al., 2009). -/- In an RBE, the law of balance would guide the sustainable use of resources. Every decision, from production to distribution, would be evaluated in terms of its environmental impact and its ability to sustain the system’s ecological health over the long term. The goal is to manage resources within the planetary boundaries while ensuring that the needs of all humans are met (Steffen et al., 2015). -/- 2. Feedback Mechanisms in Decision-Making -/- Malicse’s universal formula emphasizes the importance of feedback mechanisms in decision-making processes. This concept aligns with cybernetic theory, where systems are viewed as self-regulating through continuous feedback loops (Wiener, 1948). In the context of an RBE, feedback mechanisms ensure that resource management and societal functions are responsive to both human and ecological needs. -/- Technological systems, such as smart grids, environmental sensors, and data-driven agricultural systems, can collect real-time data on resource use, environmental conditions, and societal needs. This information feeds into decision-making processes, enabling real-time adjustments in resource allocation and use. By utilizing technology to monitor environmental and societal conditions, feedback ensures that the RBE operates in harmony with its ecological and social context, preventing wasteful practices and correcting imbalances as they occur. -/- 3. Decision-Making Governed by Natural Laws -/- The third key principle of Malicse’s universal formula is that decision-making must be grounded in natural laws, particularly the law of balance and the cause-and-effect principle. In a traditional market economy, decisions are often driven by profit motives and speculative financial interests, which can lead to unsustainable practices and social inequality. These systems disregard the long-term consequences of their actions, which leads to environmental damage, economic instability, and social unrest (Korten, 2006). -/- In an RBE governed by Malicse’s formula, all decisions—whether they concern resource allocation, infrastructure development, or social programs—must adhere to the principles of natural law. This ensures that all actions are aligned with ecological sustainability and social well-being. For instance, resource extraction decisions would not be made based on short-term profit but based on ecological science and long-term sustainability, ensuring that the Earth’s natural capital is preserved for future generations. -/- III. Integrating the Universal Formula into RBE Governance -/- 1. Role of the Universal Formula as a Constitutional Principle -/- In an RBE, Malicse’s universal formula would serve as the core constitutional principle that guides all governance structures. Just as national constitutions enshrine fundamental values such as justice, liberty, and equality, the universal formula would enshrine principles of ecological balance, social equity, and ethical decision-making. All public policies, economic models, and technological innovations would be evaluated through the lens of Malicse’s formula to ensure they contribute to a sustainable, just, and balanced society. -/- For instance, urban development projects would be designed to minimize environmental impact, incorporate renewable energy sources, and meet the needs of all community members. Policies would be implemented to ensure that economic activities are in line with the carrying capacity of the planet, and that human activities do not exceed ecological thresholds (Rockström et al., 2009). -/- 2. The Universal Formula in AI and Technological Systems -/- AI and technological systems would play a critical role in an RBE governed by natural laws. Under Malicse’s formula, AI systems and automated processes would be programmed to prioritize the principles of balance and feedback. For example, resource allocation algorithms could ensure that resources are distributed equitably and sustainably, while environmental monitoring systems could adjust production practices based on real-time data about resource availability and ecological health. -/- This concept is supported by cybernetic theory, where systems are designed to optimize based on feedback from their environment. By using AI and sensors, the RBE would be able to respond dynamically to changing ecological and societal conditions, continuously adjusting policies and practices to maintain balance. -/- 3. The Universal Formula as the Educational Foundation -/- For the principles of Malicse’s universal formula to be successfully implemented, they must first be ingrained in educational systems. Current educational frameworks often prioritize competition, individualism, and economic growth—values that are incongruent with the principles of sustainability and collective well-being (Orr, 1992). In an RBE, however, systems thinking and ecological literacy would form the foundation of education. -/- Students would be taught not only about scientific principles and natural laws, but also about the ethical responsibility humans have to maintain balance with the Earth’s ecosystems. This foundational education would produce future leaders, technocrats, and citizens who understand that decision-making must align with the long-term health of both society and the planet. By instilling these values from an early age, society can create a population that is equipped to address the complex challenges of living sustainably within ecological limits (Davidson, 2017). -/- IV. Transitioning from Capitalism to a Resource-Based Economy -/- 1. Educational and Technological Transformation -/- The transition from a capitalist model to an RBE based on Malicse’s formula would require both educational and technological transformations. First, the educational system would need to be restructured to teach systems thinking, ecological balance, and ethical decision-making. Schools, universities, and training programs would focus on sustainability, cooperation, and resource management to prepare individuals for their roles in a resource-based society. -/- Parallel to educational reform, technology would play a crucial role in managing resources efficiently. The technological infrastructure of an RBE would rely on feedback-driven systems, such as automated resource allocation, smart infrastructure, and sustainability-monitoring technologies. These technologies would ensure that resource use is optimized and that the economy operates within ecological limits. -/- 2. Governance and Policy Reform -/- In terms of governance, the transition to an RBE would involve policy reform that aligns with the principles of balance, feedback, and natural law. Governments would be responsible for ensuring that policies foster sustainability, equity, and environmental health. Policies would also ensure that social programs, such as healthcare, education, and public services, are accessible to all and are aligned with ecological sustainability. -/- Additionally, governance structures would need to move away from profit-driven politics and toward a more ethical, cooperative, and feedback-based decision-making system. The goal would be to create a political system that prioritizes the well-being of all people and the health of the planet (Raworth, 2017). -/- V. Conclusion -/- The integration of Angelito Malicse’s universal formula into a Resource-Based Economy offers a robust framework for creating a society that operates within ecological limits, promotes human well-being, and adheres to natural laws. By emphasizing balance, feedback, and natural law, this approach provides an alternative to the profit-driven, unsustainable economic systems that dominate the world today. The successful implementation of this framework requires education reform, technological innovation, and policy change to align societal practices with the values of sustainability and cooperation. Ultimately, the universal formula offers a pathway to a just, sustainable, and equitable future, where human activities are harmonized with the natural world. -/- References -/- Daly, H. E. (1991). Steady-State Economics: The Economics of Biophysical Equilibrium and Moral Growth. -/- Davidson, C. (2017). Ecological Literacy: Education and the Transition to a Postmodern World. -/- Fresco, J. (2012). The Venus Project: The Redesign of a Culture. -/- Korten, D. C. (2006). The Great Turning: From Empire to Earth Community. -/- Meadows, D. H., Meadows, D. L., & Randers, J. (2004). Limits to Growth: The 30-Year Update. -/- Raworth, K. (2017). Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist. -/- Rockström, J., et al. (2009). A Safe Operating Space for Humanity. Nature, 461(7263), 472-475. -/- Wiener, N. (1948). Cybernetics: Or Control and Communication in the Animal and the Machine. -/- . (shrink)

Title: A Model for a Moneyless Digital Economy Using Contribution-Based Digital Credits -/- Abstract: This paper proposes a conceptual framework for a moneyless digital economy system (MDES) that uses non-tradable contribution credits instead of traditional currency. Rooted in the principles of equitable access, contribution-driven participation, and sustainability, this model outlines the structure and functioning of a community-oriented, AI-assisted economy. The system ensures basic needs are universally guaranteed, while elective resources are accessed through earned credits based on meaningful contribution. The (...) model is illustrated through a community simulation and supported by technological and social mechanisms. -/- 1. Introduction The current global economic system, largely driven by capital accumulation and monetary transactions, often fails to equitably distribute resources or prioritize collective well-being. In contrast, a moneyless digital economy offers an alternative paradigm that emphasizes shared access, contribution, and sustainability. This paper presents a formal model for implementing such a system using digital technologies and community governance mechanisms. -/- 2. Core Principles -/- Access-Based Economy: Resources are accessed based on need and contribution, not purchased. -/- Contribution-Driven Credit System: Digital credits are awarded for contributions to the community but are not used for wealth accumulation. -/- Equal Human Value: All individuals are treated equally, and credits do not confer social or economic status. -/- AI-Assisted Fairness: Artificial intelligence supports equitable resource distribution and conflict resolution. -/- 3. System Components -/- 3.1 Digital Identity Each participant is issued a secure digital identity. This identity is used to track contributions, resource usage, and preferences, while ensuring privacy and security. 3.2 3.3 Contribution Credit System (CCS) Contribution credits (CCs) are non-transferable digital units earned through volunteer work, service, education, innovation, and caregiving. These credits are temporary and expire to prevent hoarding. 3.4 3.3 Access Management System Resources are categorized as guaranteed or elective. Guaranteed resources (e.g., food, housing, healthcare) are freely accessible. Elective resources require a small number of CCs and are managed by a fair allocation algorithm. -/- 3.5 AI & Community Governance An AI system manages logistics, optimizes task distribution, and ensures resource availability. Community governance bodies provide oversight and facilitate democratic decision-making. 3.6 3.5 Basic Needs Guarantee Universal access to food, shelter, healthcare, education, and digital tools is ensured without requiring credits. -/- 4. Simulation: Harmony Village A prototype implementation of MDES is simulated in a fictional community, Harmony Village, with 1,000 residents. -/- Roles & Contributions: Participants fulfill roles in health, education, infrastructure, technology, agriculture, and arts, earning between 2 and 4 CCs daily based on effort and community impact. -/- Resource Access: Basic needs are universally accessible. Elective resources (e.g., virtual reality labs, travel, private workspace) require CCs. AI systems ensure fair scheduling and availability. -/- Daily Operations: AI assigns tasks based on skills and interests. Contribution logs are updated in real-time. Community forums allow feedback and policy updates. -/- Weekly Flow Example: A typical resident, Layla, earns CCs through arts education and gardening. She uses credits to access creative tools and participate in cultural activities. Her contributions and access are balanced, transparent, and reviewed weekly by the system. -/- 5. Implementation Requirements -/- Reliable digital infrastructure (internet, secure identity verification) -/- Renewable energy and sustainable local production -/- Transparent and auditable AI governance -/- Holistic education promoting responsibility, empathy, and critical thinking -/- Community-led adaptation and governance mechanisms -/- 6. Conclusion The MDES model offers a viable framework for transitioning to a post-monetary economy focused on equity, sustainability, and human dignity. With the integration of digital identity, contribution tracking, and AI-assisted management, societies can reimagine access and participation beyond conventional monetary constraints. -/- Future research may include real-world pilot programs, development of decentralized digital infrastructure, and sociological studies of community adaptation to non-monetary systems. -/- . (shrink)

Machine learning (ML) models have become integral to decision-making processes across various sectors, including healthcare, finance, and criminal justice. However, these models often inherit and even amplify biases present in training data, leading to unfair outcomes for certain demographic groups. This paper critically examines the ethical implications of bias and fairness in ML models, exploring the sources of bias, its impact on marginalized communities, and the ethical challenges it poses. We review recent literature to identify common biases in ML systems, (...) such as racial, gender, and socioeconomic biases, and discuss the consequences of these biases in realworld applications. Furthermore, we evaluate existing fairness metrics and mitigation strategies, highlighting their strengths and limitations. The paper also discusses the role of transparency, accountability, and regulation in addressing these ethical concerns. Through this examination, we aim to provide a comprehensive understanding of the ethical dimensions of bias and fairness in ML models and propose pathways toward more equitable AI systems. (shrink)

Artificial Intelligence (AI) and Machine Learning (ML) are transforming the insurance industry by optimizing risk assessment, fraud detection, and customer service. However, the rapid adoption of these technologies raises significant ethical concerns, particularly regarding bias and fairness. This chapter explores the ethical challenges of using AI and ML in insurance risk management, focusing on bias mitigation and fairness enhancement strategies. By analyzing real-world case studies, regulatory frameworks, and technical methodologies, this chapter aims to provide a roadmap for developing ethical AI/ML (...) systems in the insurance sector. It highlights the importance of transparency, accountability, and inclusivity in ensuring equitable outcomes. (shrink)

Algorithms can now identify patterns and correlations in the (big) datasets, and predict outcomes based on those identified patterns and correlations with the use of machine learning techniques and big data, decisions can then be made by algorithms themselves in accordance with the predicted outcomes. Yet, algorithms can inherit questionable values from the datasets and acquire biases in the course of (machine) learning, and automated algorithmic decision-making makes it more difficult for people to see algorithms as (...) biased. While researchers have taken the problem of algorithmic bias seriously, but the current discussion on algorithmic fairness tends to conceptualize ‘fairness’ in algorithmic fairness primarily as a technical issue and attempts to implement pre-existing ideas of ‘fairness’ into algorithms. In this paper, I show that such a view of algorithmic fairness as technical issue is unsatisfactory for the type of problem algorithmic fairness presents. Since decisions on fairness measure and the related techniques for algorithms essentially involve choices between competing values, ‘fairness’ in algorithmic fairness should be conceptualized first and foremost as a political issue, and it should be (re)solved by democratic communication. The aim of this paper, therefore, is to explicitly reconceptualize algorithmic fairness as a political question and suggest the current discussion of algorithmic fairness can be strengthened by adopting the accountability for reasonableness framework. (shrink)

In information societies, operations, decisions and choices previously left to humans are increasingly delegated to algorithms, which may advise, if not decide, about how data should be interpreted and what actions should be taken as a result. More and more often, algorithms mediate social processes, business transactions, governmental decisions, and how we perceive, understand, and interact among ourselves and with the environment. Gaps between the design and operation of algorithms and our understanding of their ethical implications can (...) have severe consequences affecting individuals as well as groups and whole societies. This paper makes three contributions to clarify the ethical importance of algorithmic mediation. It provides a prescriptive map to organise the debate. It reviews the current discussion of ethical aspects of algorithms. And it assesses the available literature in order to identify areas requiring further work to develop the ethics of algorithms. (shrink)

As algorithms have become ubiquitous in consequential domains, societal concerns about the potential for discriminatory outcomes have prompted urgent calls to address algorithmic bias. In response, a rich literature across computer science, law, and ethics is rapidly proliferating to advance approaches to designing fair algorithms. Yet computer scientists, legal scholars, and ethicists are often not speaking the same language when using the term ‘bias.’ Debates concerning whether society can or should tackle the problem of algorithmic bias are hampered (...) by conflations of various understandings of bias, ranging from neutral deviations from a standard to morally problematic instances of injustice due to prejudice, discrimination, and disparate treatment. This terminological confusion impedes efforts to address clear cases of discrimination. -/- In this paper, we examine the promises and challenges of different approaches to disambiguating bias and designing for justice. While both approaches aid in understanding and addressing clear algorithmic harms, we argue that they also risk being leveraged in ways that ultimately deflect accountability from those building and deploying these systems. Applying this analysis to recent examples of generative AI, our argument highlights unseen dangers in current methods of evaluating algorithmic bias and points to ways to redirect approaches to addressing bias in generative AI at its early stages in ways that can more robustly meet the demands of justice. (shrink)

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After (...) first clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

It is well-established that algorithms can be instruments of injustice. It is less frequently discussed, however, how current modes of AI deployment often make the very discovery of injustice difficult, if not impossible. In this article, we focus on the effects of algorithmic profiling on epistemic agency. We show how algorithmic profiling can give rise to epistemic injustice through the depletion of epistemic resources that are needed to interpret and evaluate certain experiences. By doing so, we not only demonstrate (...) how the philosophical conceptual framework of epistemic injustice can help pinpoint potential, systematic harms from algorithmic profiling, but we also identify a novel source of hermeneutical injustice that to date has received little attention in the relevant literature: epistemic fragmentation. Epistemic fragmentation is a structural characteristic of algorithmically-mediated environments that isolate individuals, making it more difficult to develop, uptake and apply new epistemic resources. This, in turn, can impede the identification and conceptualisation of emerging harms in these environments. We trace the occurrence of hermeneutical injustice back to the fragmentation of the epistemic experiences of individuals, who are left more vulnerable by the inability to share, compare, and learn from shared experiences. (shrink)

Algorithmic systems and predictive analytics play an increasingly important role in various aspects of modern life. Scholarship on the moral ramifications of such systems is in its early stages, and much of it focuses on bias and harm. This paper argues that in understanding the moral salience of algorithmic systems it is essential to understand the relation between algorithms, autonomy, and agency. We draw on several recent cases in criminal sentencing and K–12 teacher evaluation to outline four key ways (...) in which issues of agency, autonomy, and respect for persons can conflict with algorithmic decision-making. Three of these involve failures to treat individual agents with the respect they deserve. The fourth involves distancing oneself from a morally suspect action by attributing one’s decision to take that action to an algorithm, thereby laundering one’s agency. (shrink)

Algorithms wield increasing control over our lives—over the jobs we get, the loans we're granted, the information we see online. Algorithms can and often do wield their power in a biased way, and much work has been devoted to algorithmic bias. In contrast, algorithmic neutrality has been largely neglected. I investigate algorithmic neutrality, tackling three questions: What is algorithmic neutrality? Is it possible? And when we have it in mind, what can we learn about algorithmic bias?

Statistical fairness criteria are widely used for diagnosing and ameliorating algorithmic bias. However, these fairness criteria are controversial as their use raises several difficult questions. I argue that the major problems for statistical algorithmic fairness criteria stem from an incorrect understanding of their nature. These criteria are primarily used for two purposes: first, evaluating AI systems for bias, and second constraining machine learning optimization problems in order to ameliorate such bias. The first purpose typically involves treating each criterion as a (...) necessary condition for fairness. The second use involves treating criteria as sufficient conditions for fairness. Since the criteria are used for both roles, some researchers have treated them as both necessary and sufficient conditions, i.e., as definitions of algorithmic fairness. However, serious problems have been raised for the use of these fairness criteria. Under ordinary circumstances, it is impossible to satisfy multiple criteria at the same time. Moreover, there are counterexamples to both the sufficiency and necessity for fairness of each criterion. I argue that we should instead understand fairness criteria as merely providing evidence of fairness. In other words, satisfaction (or violation) of these criteria should be understood as potential evidence of fairness (or bias). Whether a criterion counts as evidence in a particular case will depend on stakeholders' background knowledge and the specific features of the system's task. This evidence account of fairness conditions provides guidance for recognizing both the appropriate uses and the limitations of fairness criteria. (shrink)

The stakes associated with an algorithmic decision are often said to play a role in determining whether the decision engenders a right to an explanation. More specifically, “high stakes” decisions are often said to engender such a right to explanation whereas “low stakes” or “non-high” stakes decisions do not. While the overall gist of these ideas is clear enough, the details are lacking. In this paper, we aim to provide these details through a detailed investigation of what we will call (...) the “Simple Stakes Thesis”. The Simple Stakes Thesis, as it will turn out, is too simple. For even if the stakes associated with a specific one-off decision are low—and hence does not engender a right to an explanation—such decisions may nevertheless form part of a high stakes pattern or aggregate of decisions. In such cases, we argue, even a low stakes decision may engender a right to explanation. Not only does this show that the right to explanation is more demanding than so far recognized. It also shows that the stakes thesis is significantly harder to apply in practice. (shrink)

Algorithms influence every facet of modern life: criminal justice, education, housing, entertainment, elections, social media, news feeds, work… the list goes on. Delegating important decisions to machines, however, gives rise to deep moral concerns about responsibility, transparency, freedom, fairness, and democracy. Algorithms and Autonomy connects these concerns to the core human value of autonomy in the contexts of algorithmic teacher evaluation, risk assessment in criminal sentencing, predictive policing, background checks, news feeds, ride-sharing platforms, social media, and election interference. (...) Using these case studies, the authors provide a better understanding of machine fairness and algorithmic transparency. They explain why interventions in algorithmic systems are necessary to ensure that algorithms are not used to control citizens' participation in politics and undercut democracy. This title is also available as Open Access on Cambridge Core. (shrink)

Inspired by recent breakthroughs in predictive modeling, practitioners in both industry and government have turned to machine learning with hopes of operationalizing predictions to drive automated decisions. Unfortunately, many social desiderata concerning consequential decisions, such as justice or fairness, have no natural formulation within a purely predictive framework. In efforts to mitigate these problems, researchers have proposed a variety of metrics for quantifying deviations from various statistical parities that we might expect to observe in a fair world and offered a (...) variety of algorithms in attempts to satisfy subsets of these parities or to trade o the degree to which they are satised against utility. In this paper, we connect this approach to fair machine learning to the literature on ideal and non-ideal methodological approaches in political philosophy. The ideal approach requires positing the principles according to which a just world would operate. In the most straightforward application of ideal theory, one supports a proposed policy by arguing that it closes a discrepancy between the real and the perfectly just world. However, by failing to account for the mechanisms by which our non-ideal world arose, the responsibilities of various decision-makers, and the impacts of proposed policies, naive applications of ideal thinking can lead to misguided interventions. In this paper, we demonstrate a connection between the fair machine learning literature and the ideal approach in political philosophy, and argue that the increasingly apparent shortcomings of proposed fair machine learning algorithms reflect broader troubles faced by the ideal approach. We conclude with a critical discussion of the harms of misguided solutions, a reinterpretation of impossibility results, and directions for future research. (shrink)

The prospective introduction of autonomous cars into public traffic raises the question of how such systems should behave when an accident is inevitable. Due to concerns with self-interest and liberal legitimacy that have become paramount in the emerging debate, a contractarian framework seems to provide a particularly attractive means of approaching this problem. We examine one such attempt, which derives a harm minimisation rule from the assumptions of rational self-interest and ignorance of one’s position in a future accident. We contend, (...) however, that both contractarian approaches and harm minimisation standards are flawed, due to a failure to account for the fundamental difference between those ‘involved’ and ‘uninvolved’ in an impending crash. Drawing from classical works on the trolley problem, we show how this notion can be substantiated by reference to either the distinction between negative and positive rights, or to differences in people’s claims. By supplementing harm minimisation with corresponding constraints, we can develop crash algorithms for autonomous cars which are both ethically adequate and promise to overcome certain significant practical barriers to implementation. (shrink)

Machine intelligence already helps medical staff with a number of tasks. Ethical decision-making, however, has not been handed over to computers. In this proof-of-concept study, we show how an algorithm based on Beauchamp and Childress’ prima-facie principles could be employed to advise on a range of moral dilemma situations that occur in medical institutions. We explain why we chose fuzzy cognitive maps to set up the advisory system and how we utilized machine learning to train it. We report on the (...) difficult task of operationalizing the principles of beneficence, non-maleficence and patient autonomy, and describe how we selected suitable input parameters that we extracted from a training dataset of clinical cases. The first performance results are promising, but an algorithmic approach to ethics also comes with several weaknesses and limitations. Should one really entrust the sensitive domain of clinical ethics to machine intelligence? (shrink)

In Algorithms & Autonomy, Rubel, Castro, and Pham (hereafter RCP), argue that the concept of autonomy is especially central to understanding important moral problems about algorithms. In particular, autonomy plays a role in analyzing the version of social contract theory that they endorse. I argue that although RCP are largely correct in their diagnosis of what is wrong with the algorithms they consider, those diagnoses can be appropriated by moral theories RCP see as in competition with their (...) autonomy based theory. Most notably, proponents of consequentialism and virtue ethics can appropriate RCPs insights. This is good news because RCP’s social contract theory is vulnerable to a well known class of counterexamples. The most significant contribution of RCP, if I am right, is in their identification, presentation, and evaluation of concrete cases involving algorithms and not in the more controversial claims about theoretical ethics that RCP themselves see as central to what they are doing. (shrink)

This introduction to a special section on algorithmic thought provides a framework through which the articles in that collection can be contextualised and their individual contributions highlighted. Over the past decade, there has been a growing interest in artificial intelligence (AI). This special section reflects on this AI boom and its implications for studying what thinking is. Focusing on the algorithmic character of computing machines and the thinking that these machines might express, each of the special section’s essays considers different (...) dimensions of algorithmic thought, engaging with a diverse set of epistemological questions and issues. (shrink)

Artificial intelligence (AI) systems are increasingly adopted to make decisions in domains such as business, education, health care, and criminal justice. However, such algorithmic decision systems can have prevalent biases against marginalized social groups and undermine social justice. Explainable artificial intelligence (XAI) is a recent development aiming to make an AI system’s decision processes less opaque and to expose its problematic biases. This paper argues against technical XAI, according to which the detection and interpretation of algorithmic bias can be handled (...) more or less independently by technical experts who specialize in XAI methods. Drawing on resources from feminist epistemology, we show why technical XAI is mistaken. Specifically, we demonstrate that the proper detection of algorithmic bias requires relevant interpretive resources, which can only be made available, in practice, by actively involving a diverse group of stakeholders. Finally, we suggest how feminist theories can help shape integrated XAI: an inclusive social-epistemic process that facilitates the amelioration of algorithmic bias. (shrink)

In recent years, the ethical impact of AI has been increasingly scrutinized, with public scandals emerging over biased outcomes, lack of transparency, and the misuse of data. This has led to a growing mistrust of AI and increased calls for mandated ethical audits of algorithms. Current proposals for ethical assessment of algorithms are either too high level to be put into practice without further guidance, or they focus on very specific and technical notions of fairness or transparency that (...) do not consider multiple stakeholders or the broader social context. In this article, we present an auditing framework to guide the ethical assessment of an algorithm. The audit instrument itself is comprised of three elements: a list of possible interests of stakeholders affected by the algorithm, an assessment of metrics that describe key ethically salient features of the algorithm, and a relevancy matrix that connects the assessed metrics to stakeholder interests. The proposed audit instrument yields an ethical evaluation of an algorithm that could be used by regulators and others interested in doing due diligence, while paying careful attention to the complex societal context within which the algorithm is deployed. (shrink)

In a previous work we introduced the algorithm \SQEMA\ for computing first-order equivalents and proving canonicity of modal formulae, and thus established a very general correspondence and canonical completeness result. \SQEMA\ is based on transformation rules, the most important of which employs a modal version of a result by Ackermann that enables elimination of an existentially quantified predicate variable in a formula, provided a certain negative polarity condition on that variable is satisfied. In this paper we develop several extensions of (...) \SQEMA\ where that syntactic condition is replaced by a semantic one, viz. downward monotonicity. For the first, and most general, extension \SSQEMA\ we prove correctness for a large class of modal formulae containing an extension of the Sahlqvist formulae, defined by replacing polarity with monotonicity. By employing a special modal version of Lyndon's monotonicity theorem and imposing additional requirements on the Ackermann rule we obtain restricted versions of \SSQEMA\ which guarantee canonicity, too. (shrink)

Does algorithmic political bias contribute to an entrenchment and polarization of political positions? Franke argues that it may do so because the bias involves classifications of people as liberals, conservatives, etc., and individuals often conform to the ways in which they are classified. I provide a novel example of this phenomenon in human–computer interactions and introduce a social psychological mechanism that has been overlooked in this context but should be experimentally explored. Furthermore, while Franke proposes that algorithmic political classifications entrench (...) political identities, I contend that they may often produce the opposite result. They can lead people to change in ways that disconfirm the classifications. Consequently and counterintuitively, algorithmic political bias can in fact decrease political entrenchment and polarization. (shrink)

Predictive algorithms are playing an increasingly prominent role in society, being used to predict recidivism, loan repayment, job performance, and so on. With this increasing influence has come an increasing concern with the ways in which they might be unfair or biased against individuals in virtue of their race, gender, or, more generally, their group membership. Many purported criteria of algorithmic fairness concern statistical relationships between the algorithm’s predictions and the actual outcomes, for instance requiring that the rate of (...) false positives be equal across the relevant groups. We might seek to ensure that algorithms satisfy all of these purported fairness criteria. But a series of impossibility results shows that this is impossible, unless base rates are equal across the relevant groups. What are we to make of these pessimistic results? I argue that none of the purported criteria, except for a calibration criterion, are necessary conditions for fairness, on the grounds that they can all be simultaneously violated by a manifestly fair and uniquely optimal predictive algorithm, even when base rates are equal. I conclude with some general reflections on algorithmic fairness. (shrink)

The paper offers a solution to the generality problem for a reliabilist epistemology, by developing an “algorithm and parameters” scheme for type-individuating cognitive processes. Algorithms are detailed procedures for mapping inputs to outputs. Parameters are psychological variables that systematically affect processing. The relevant process type for a given token is given by the complete algorithmic characterization of the token, along with the values of all the causally relevant parameters. The typing that results is far removed from the typings of (...) folk psychology, and from much of the epistemology literature. But it is principled and empirically grounded, and shows good prospects for yielding the desired epistemological verdicts. The paper articulates and elaborates the theory, drawing out some of its consequences. Toward the end, the fleshed-out theory is applied to two important case studies: hallucination and cognitive penetration of perception. (shrink)

We argue that machine learning algorithms can inflict microaggressions on members of marginalized groups and that recognizing these harms as instances of microaggressions is key to effectively addressing the problem. The concept of microaggression is also illuminated by being studied in algorithmic contexts. We contribute to the microaggression literature by expanding the category of environmental microaggressions and highlighting the unique issues of moral responsibility that arise when we focus on this category. We theorize two kinds of algorithmic microaggression, stereotyping (...) and erasure microaggressions, and argue that corporations are responsible for the microaggressions their algorithms create. As a case study, we look at the problems faced by Google’s autocomplete prediction and at the insufficiency of their solutions. The case study of autocomplete demonstrates our core claim that microaggressions constitute a distinct form of algorithmic bias and that identifying them as such allows us to avoid seeming solutions that recreate the same kinds of harms. Google has a responsibility to make information freely available, without exposing users to degradation. To fulfill its duties to marginalized groups, Google must abandon the fiction of neutral prediction and instead embrace the liberatory power of suggestion. (shrink)

The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. (...) In this paper, we provide the building blocks for an account of algorithmic bias and its normative relevance in medicine. (shrink)

Argumentation theory underwent a significant development in the Fifties and Sixties: its revival is usually connected to Perelman's criticism of formal logic and the development of informal logic. Interestingly enough it was during this period that Artificial Intelligence was developed, which defended the following thesis (from now on referred to as the AI-thesis): human reasoning can be emulated by machines. The paper suggests a reconstruction of the opposition between formal and informal logic as a move against a premise of an (...) argument for the AI-thesis, and suggests making a distinction between a broad and a narrow notion of algorithm that might be used to reformulate the question as a foundational problem for argumentation theory. (shrink)

I argue that the thoroughly algorithmic nature of current AI systems (such as LLMs) is no obstacle to their being conscious. To this end, I present a picture on which current AI systems comprise dispositional properties which realize categorical phenomenal properties where the latter, in turn, provide the identity conditions for their dispositional realizers. This mutual ontological dependence, or, symmetrical grounding, at the heart of the proposal yields a novel picture of (AI) consciousness that avoids epiphenomenalism and is more permissive (...) regarding the specific nature/functional organization of conscious systems than has been previously suggested. This, in turn, suggests an epistemology of AI consciousness focused on investigating the high-level behaviours of AI systems rather than their low-level functional organization. (shrink)

Data-driven predictive algorithms are widely used to automate and guide high-stake decision making such as bail and parole recommendation, medical resource distribution, and mortgage allocation. Nevertheless, harmful outcomes biased against vulnerable groups have been reported. The growing research field known as 'algorithmic fairness' aims to mitigate these harmful biases. Its primary methodology consists in proposing mathematical metrics to address the social harms resulting from an algorithm's biased outputs. The metrics are typically motivated by -- or substantively rooted in -- (...) ideals of distributive justice, as formulated by political and legal philosophers. The perspectives of feminist political philosophers on social justice, by contrast, have been largely neglected. Some feminist philosophers have criticized the local scope of the paradigm of distributive justice and have proposed corrective amendments to surmount its limitations. The present paper brings some key insights of feminist political philosophy to algorithmic fairness. The paper has three goals. First, I show that algorithmic fairness does not accommodate structural injustices in its current scope. Second, I defend the relevance of structural injustices -- as pioneered in the contemporary philosophical literature by Iris Marion Young -- to algorithmic fairness. Third, I take some steps in developing the paradigm of 'responsible algorithmic fairness' to correct for errors in the current scope and implementation of algorithmic fairness. I close by some reflections of directions for future research. (shrink)

In this paper, we distinguish between different sorts of assessments of algorithmic systems, describe our process of assessing such systems for ethical risk, and share some key challenges and lessons for future algorithm assessments and audits. Given the distinctive nature and function of a third-party audit, and the uncertain and shifting regulatory landscape, we suggest that second-party assessments are currently the primary mechanisms for analyzing the social impacts of systems that incorporate artificial intelligence. We then discuss two kinds of as-sessments: (...) an ethical risk assessment and a narrower, technical algo-rithmic bias assessment. We explain how the two assessments depend on each other, highlight the importance of situating the algorithm within its particular socio-technical context, and discuss a number of lessons and challenges for algorithm assessments and, potentially, for algorithm audits. The discussion builds on our team’s experience of advising and conducting ethical risk assessments for clients across dif-ferent industries in the last four years. Our main goal is to reflect on the key factors that are potentially ethically relevant in the use of algo-rithms, and draw lessons for the nascent algorithm assessment and audit industry, in the hope of helping all parties minimize the risk of harm from their use. (shrink)

As inductive decision-making procedures, the inferences made by machine learning programs are subject to underdetermination by evidence and bear inductive risk. One strategy for overcoming these challenges is guided by a presumption in philosophy of science that inductive inferences can and should be value-free. Applied to machine learning programs, the strategy assumes that the influence of values is restricted to data and decision outcomes, thereby omitting internal value-laden design choice points. In this paper, I apply arguments from feminist philosophy of (...) science to machine learning programs to make the case that the resources required to respond to these inductive challenges render critical aspects of their design constitutively value-laden. I demonstrate these points specifically in the case of recidivism algorithms, arguing that contemporary debates concerning fairness in criminal justice risk-assessment programs are best understood as iterations of traditional arguments from inductive risk and demarcation, and thereby establish the value-laden nature of automated decision-making programs. Finally, in light of these points, I address opportunities for relocating the value-free ideal in machine learning and the limitations that accompany them. (shrink)

This article examines the complaint that arbitrary algorithmic decisions wrong those whom they affect. It makes three contributions. First, it provides an analysis of what arbitrariness means in this context. Second, it argues that arbitrariness is not of moral concern except when special circumstances apply. However, when the same algorithm or different algorithms based on the same data are used in multiple contexts, a person may be arbitrarily excluded from a broad range of opportunities. The third contribution is to (...) explain why this systemic exclusion is of moral concern and to offer a solution to address it. (shrink)

Algorithmic bias has been the subject of much recent controversy. To clarify what is at stake and to make progress resolving the controversy, a better understanding of the concepts involved would be helpful. The discussion here focuses on the disputed claim that algorithms themselves cannot be biased. To clarify this claim we need to know what kind of thing ‘algorithms themselves’ are, and to disambiguate the several meanings of ‘bias’ at play. This further involves showing how bias of (...) moral import can result from statistical biases, and drawing connections to previous conceptual work about political artifacts and oppressive things. Data bias has been identified in domains like hiring, policing and medicine. Examples where algorithms themselves have been pinpointed as the locus of bias include recommender systems that influence media consumption, academic search engines that influence citation patterns, and the 2020 UK algorithmically-moderated A-level grades. Recognition that algorithms are a kind of thing that can be biased is key to making decisions about responsibility for harm, and preventing algorithmically mediated discrimination. (shrink)

Research on the ethics of algorithms has grown substantially over the past decade. Alongside the exponential development and application of machine learning algorithms, new ethical problems and solutions relating to their ubiquitous use in society have been proposed. This article builds on a review of the ethics of algorithms published in 2016, 2016). The goals are to contribute to the debate on the identification and analysis of the ethical implications of algorithms, to provide an updated analysis (...) of epistemic and normative concerns, and to offer actionable guidance for the governance of the design, development and deployment of algorithms. (shrink)

We live in a world in which ‘smart’ algorithmic tools are regularly used to structure and control our choice environments. They do so by affecting the options with which we are presented and the choices that we are encouraged or able to make. Many of us make use of these tools in our daily lives, using them to solve personal problems and fulfill goals and ambitions. What consequences does this have for individual autonomy and how should our legal and regulatory (...) systems respond? This chapter defends three claims by way of response. First, it argues that autonomy is indeed under threat in some new and interesting ways. Second, it evaluates and disputes the claim that we shouldn’t overestimate these new threats because the technology is just an old wolf in a new sheep’s clothing. Third, and finally, it looks at responses to these threats at both the individual and societal level and argues that although we shouldn’t encourage an attitude of ‘helplessness’ among the users of algorithmic tools there is an important role for legal and regulatory responses to these threats that go beyond what are currently on offer. (shrink)

We consider two ways one might use algorithmic randomness to characterize a probabilistic law. The first is a generative chance* law. Such laws involve a nonstandard notion of chance. The second is a probabilistic* constraining law. Such laws impose relative frequency and randomness constraints that every physically possible world must satisfy. While each notion has virtues, we argue that the latter has advantages over the former. It supports a unified governing account of non-Humean laws and provides independently motivated solutions to (...) issues in the Humean best-system account. On both notions, we have a much tighter connection between probabilistic laws and their corresponding sets of possible worlds. Certain histories permitted by traditional probabilistic laws are ruled out as physically impossible. As a result, such laws avoid one variety of empirical underdetermination, but the approach reveals other varieties of underdetermination that are typically overlooked. (shrink)

Some artificial intelligence systems can display algorithmic bias, i.e. they may produce outputs that unfairly discriminate against people based on their social identity. Much research on this topic focuses on algorithmic bias that disadvantages people based on their gender or racial identity. The related ethical problems are significant and well known. Algorithmic bias against other aspects of people’s social identity, for instance, their political orientation, remains largely unexplored. This paper argues that algorithmic bias against people’s political orientation can arise in (...) some of the same ways in which algorithmic gender and racial biases emerge. However, it differs importantly from them because there are strong social norms against gender and racial biases. This does not hold to the same extent for political biases. Political biases can thus more powerfully influence people, which increases the chances that these biases become embedded in algorithms and makes algorithmic political biases harder to detect and eradicate than gender and racial biases even though they all can produce similar harm. Since some algorithms can now also easily identify people’s political orientations against their will, these problems are exacerbated. Algorithmic political bias thus raises substantial and distinctive risks that the AI community should be aware of and examine. (shrink)

We cooperate with other people despite the risk of being exploited or hurt. If future artificial intelligence (AI) systems are benevolent and cooperative toward us, what will we do in return? Here we show that our cooperative dispositions are weaker when we interact with AI. In nine experiments, humans interacted with either another human or an AI agent in four classic social dilemma economic games and a newly designed game of Reciprocity that we introduce here. Contrary to the hypothesis that (...) people mistrust algorithms, participants trusted their AI partners to be as cooperative as humans. However, they did not return AI's benevolence as much and exploited the AI more than humans. These findings warn that future self-driving cars or co-working robots, whose success depends on humans' returning their cooperativeness, run the risk of being exploited. This vulnerability calls not just for smarter machines but also better human-centered policies. (shrink)

Philosophers have established that certain ethically important val- ues are modally robust in the sense that they systematically deliver correlative benefits across a range of counterfactual scenarios. In this paper, we contend that recourse – the systematic process of reversing unfavorable decisions by algorithms and bureaucracies across a range of counterfactual scenarios – is such a modally ro- bust good. In particular, we argue that two essential components of a good life – temporally extended agency and trust – are (...) under- written by recourse. We critique existing approaches to the conceptualization, op- erationalization and implementation of recourse. Based on these criticisms, we suggest a revised approach to recourse and give ex- amples of how it might be implemented – especially for those who are least well off. (shrink)

The use of machine learning algorithms has become ubiquitous in hiring decisions. Recent studies have shown that many of these algorithms generate unlawful discriminatory effects in every step of the process. The training phase of the machine learning models used in these decisions has been identified as the main source of bias. For a long time, discrimination cases have been analyzed under the banner of disparate treatment and disparate impact, but these concepts have been shown to be ineffective (...) in the context of AI. This paper examines the possibility of studying algorithmic discrimination from the perspective of negligence law. Negligent selection and validation of datasets and classifiers is a salient cause of improper training in machine learning. It is a breach of an employer’s duty to protect others, a failure of its exercise of due care in the manner of choosing employees. Adopting negligence to algorithmic discrimination requires a reformulation of what is foreseeable and reasonable within the context of black box algorithms, and a shift in the burden of proof to the employer, who must present evidence of its efforts to avoid bias. (shrink)

Love is often seen as the most intimate aspect of our lives, but it is increasingly engineered by a few programmers with Artificial Intelligence (AI). Nowadays, numerous dating platforms are deploying so-called smart algorithms to identify a greater number of potential matches for a user. These AI-enabled matchmaking systems, driven by a rich trove of data, can not only predict what a user might prefer but also deeply shape how people choose their partners. This paper draws on Jürgen Habermas’s (...) “colonization of the lifeworld” thesis to critically explore the insidious influence of delegating romantic decision-making to an algorithm. The love lifeworld is colonized inasmuch as online dating algorithms encroach into our romantic relations to the extent that communicative action within romance is replaced by the technocratic rules of an algorithm. (shrink)