Engineers are traditionally regarded as trustworthy professionals who meet exacting standards. In this chapter I begin by explicating our trust relationship towards engineers, arguing that it is a linear but indirect relationship in which engineers “stand behind” the artifacts and technological systems that we rely on directly. The chapter goes on to explain how this relationship has become more complex as engineers have taken on two additional aims: the aim of social engineering to create and steer trust between (...) people, and the aim of creating automated systems that take over human tasks and are meant to invite the trust of those who rely on and interact with them. (shrink)

Algorithm Decision Making (ADM) systems designed to augment or automate human decision-making have the potential to produce better decisions while also freeing up human time and attention for other pursuits. For this potential to be realised, however, algorithmic decisions must be sufficiently aligned with human goals and interests. We take a Principal-Agent (P-A) approach to the questions of ADM alignment and trust. In a broad sense, ADM is beneficial if and only if human principals can trust algorithmic agents (...) to act faithfully on their behalf. This mirrors the challenge of facilitating P-A relationships among humans, but the peculiar nature of human-machine interaction also raises unique issues. The problem of asymmetric information is omnipresent but takes a different form in the context of ADM. Although the decision-making machinery of an algorithmic agent can in principle be laid bare for all to see, the sheer complexity of ADM systems based on deep learning models prevents straightforward monitoring. We draw on literature from economics and political science to argue that the problem of trust in ADM systems should be addressed at the level of institutions. Although the dyadic relationship between human principals and algorithmic agents is our ultimate concern, cooperation at this level must rest against an institutional environment which allows humans to effectively evaluate and choose among algorithmic alternatives. (shrink)

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

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

This abstract summarizes, in essence, what generative AI means to the insurance industry. The kind of promise generated AI offers to insurance is huge: in risk assessment, customer experience, and operational efficiency. Natural disaster impact, financial market volatility, and cyber threat are augmented with techniques of real time scenario generation and modeling as well as predictive simulation based on synthetic data. One of the challenges that stand in the way of deploying these AI methods, however, is data privacy, model reliability (...) and interpretability. The insurance industry must address these issues if it is to comply with regulations like GDPR and CCPA or mitigate reputational risks, as anybody can make complaints about any company based on any perceived data breach. Indeed, generative AI models, often convoluted and biased, can propagate unfair outcomes, especially in pricing and policy access. Navigating these challenges requires a whole new frayed society of AI specialists in collaboration with the industry’s insurers to offer bespoke solutions that address the firm’s needs. Based on this work, future developments will center on designing and implementing ethical and regulatory frameworks around AI-driven decisions that are transparent, fair and sustainable. Generative AI has historically been the most feared phenomenon in the history of western civilization, but with predictive analytics and risk simulation capabilities taking shape, generative AI is set to disrupt the insurance industry and add real-time decision support, automating claims processing and personalized interaction. To successfully integrate generative AI, technical, ethical and regulatory challenges will need to be addressed, and a trust-driven ecosystem will emerge to serve evolving customer needs. (shrink)

This paper critically assesses the possibility of moral enhancement with ambient intelligence technologies and artificial intelligence presented in Savulescu and Maslen (2015). The main problem with their proposal is that it is not robust enough to play a normative role in users’ behavior. A more promising approach, and the one presented in the paper, relies on an artifi-cial moral reasoning engine, which is designed to present its users with moral arguments grounded in first-order normative theories, such as Kantianism or utilitarianism, (...) that reason-responsive people can be persuaded by. This proposal can play a normative role and it is also a more promising avenue towards moral enhancement. It is more promising because such a system can be designed to take advantage of the sometimes undue trust that people put in automated technologies. We could therefore expect a well-designed moral reasoner system to be able to persuade people that may not be persuaded by similar arguments from other people. So, all things considered, there is hope in artificial intelli-gence for moral enhancement, but not in artificial intelligence that relies solely on ambient intelligence technologies. (shrink)

This paper sets out an account of trust in AI as a relationship between clinicians, AI applications, and AI practitioners in which AI is given discretionary authority over medical questions by clinicians. Compared to other accounts in recent literature, this account more adequately explains the normative commitments created by practitioners when inviting clinicians’ trust in AI. To avoid committing to an account of trust in AI applications themselves, I sketch a reductive view on which discretionary authority is (...) exercised by AI practitioners through the vehicle of an AI application. I conclude with four critical questions based on the discretionary account to determine if trust in particular AI applications is sound, and a brief discussion of the possibility that the main roles of the physician could be replaced by AI. (shrink)

Given a subject so imbued with contention and conflicting theoretical stances, it is remarkable that automated instruments ever came to replace the human eye as sensitive arbiters of color specification. Yet, dramatic shifts in assumptions and practice did occur in the first half of the twentieth century. How and why was confidence transferred from careful observers to mechanized devices when the property being measured – color – had become so closely identified with human physiology and psychology? A fertile perspective on (...) the problem is via the history of science and technology, paying particular attention to social groups and disciplinary identity to determine how those factors affected their communities’ cognitive territory. There were both common and discordant threads motivating the various technical groups that took on the problems of measuring light and color from the late nineteenth century onwards, and leading them towards the development of appropriate instruments for themselves. The transition from visual to photoelectric methods could be portrayed as a natural evolution, replacing the eye by an alternative roviding more sensitivity and convenience – indeed, this is the conventional positivist view propounded by technical histories. However, the adoption of new measurement technologies seldom is simple, and frequently has a significant cultural component. Beneath this slide towards automation lay a raft of implicit assumptions about objectivity, the nature of the observer, the role of instruments, and the trade-offs between standardization and descriptive power. While espousing rational arguments for a physical detector of color, its proponents weighted their views with tacit considerations. The reassignment of trust from the eye to automated instruments was influenced as much by the historical context as by intellectual factors. I will argue that several distinct aspects were involved, which include the reductive view of color provided by the trichromatic theory; the impetus provided by its association with photometry; the expanding mood for a quantitative and objective approach to scientific observation; and, the pressures for commercial standardization. As suggested by these factors, there was another shift of authority at play: from one technical specialism to another. The regularization of color involved appropriation of the subject by a particular set of social interests: communities of physicists and engineers espousing a ‘physicalist’ interpretation, rather than psychologists and physiologists for whom color was conceived as a more complex phenomenon. Moreover, the sources for automated color measurement, and instrumentation for measuring color, were primarily from the industrial sphere rather than from academic science. To understand these shifts, then, this chapter explores differing views of the importance of observers, machines and automation. (shrink)

In this chapter, we consider ethical and philosophical aspects of trust in the practice of medicine. We focus on trust within the patient-physician relationship, trust and professionalism, and trust in Western (allopathic) institutions of medicine and medical research. Philosophical approaches to trust contain important insights into medicine as an ethical and social practice. In what follows we explain several philosophical approaches and discuss their strengths and weaknesses in this context. We also highlight some relevant empirical (...) work in the section on trust in the institutions of medicine. It is hoped that the approaches discussed here can be extended to nursing and other topics in the philosophy of medicine. (shrink)

The Illusion of the Pension Crisis in a Fiat Currency System -/- Introduction -/- Governments and financial institutions frequently warn of a looming pension crisis, claiming that aging populations will drain social security funds and leave future retirees without financial support. However, this narrative is fundamentally flawed. In a fiat currency system, where central banks can print money at will, the idea that there is a shortage of funds for pensions contradicts the basic mechanics of modern finance. Instead, the so-called (...) pension problem serves as a strategic tool to justify population growth, extend working years, and maintain economic control. This essay explores how the pension crisis is manufactured, why the economic system functions this way, and what alternatives could create a more sustainable future. -/- The Fiat Currency Illusion -/- Fiat money is not backed by a physical commodity like gold but instead derives its value from government decree and public trust. Central banks, such as the U.S. Federal Reserve or the European Central Bank, have the power to create unlimited money through digital printing. If governments truly wanted to fund pensions, they could simply issue more currency. The fact that they choose not to highlights that the real issue is not about money but about power, wealth distribution, and economic control. -/- However, printing excessive money can lead to inflation, which erodes purchasing power. If pensions were funded through unrestricted money printing, prices of goods and services would rise, making pensions effectively worthless. But inflation is not an unavoidable consequence—it is often a policy decision influenced by central banks and financial elites to preserve existing economic hierarchies. -/- The Manufactured Pension ‘Crisis -/- The claim that pension systems are unsustainable is rooted in economic and political agendas rather than financial reality. The following key factors reveal why this crisis is artificially constructed: -/- 1. Inflation and Devaluation -/- While governments can print unlimited money, excessive monetary expansion can reduce the value of currency. Instead of managing inflation in a way that supports pensions, policymakers often use it to justify cuts to social programs while maintaining corporate wealth and military budgets. -/- 2. Wealth Protection for the Elite -/- The financial system is designed to benefit the wealthiest individuals and corporations. Rather than directing resources toward pensions, governments prioritize corporate bailouts, financial markets, and debt repayment. By claiming pension funds are running out, they avoid policies that redistribute wealth to the working class. -/- 3. Debt-Driven Economic Model -/- Instead of simply issuing currency for pensions, governments prefer to borrow money, ensuring central banks and private lenders remain in control. This debt-based system forces economies to keep growing, making workers dependent on employment and discouraging early retirement. -/- 4. Manipulating the Workforce Through Scarcity -/- The fear of pension collapse forces people to work longer, delaying retirement and keeping the labor force full. This benefits corporations by ensuring a steady supply of low-cost labor, preventing worker shortages, and keeping wages down. -/- 5. Stock Market and Private Pension Funds -/- Many pension funds are invested in the stock market, making their stability dependent on financial speculation rather than real economic productivity. If governments funded pensions directly, there would be less reliance on volatile financial markets, reducing Wall Street’s control over retirement savings. -/- 6. Control Through Artificial Shortages -/- By creating the illusion of limited pension funds, governments encourage population growth to “replace” aging workers. However, this ignores the real issue—technological automation and AI are reducing the need for human labor, making more workers unnecessary in the long run. -/- The Contradiction: Population Growth vs. Economic Reality -/- Governments argue that a growing population is needed to sustain pension systems. However, creating jobs and starting businesses is becoming more difficult due to several factors: -/- Automation and AI – Many traditional jobs are disappearing as machines and software replace human labor. -/- Market Saturation – Many industries are already flooded with products and services, making new business opportunities harder to find. -/- High Cost of Business Entry – Large corporations dominate markets, making it difficult for small businesses to compete. -/- Regulatory Barriers – Governments impose taxes, licenses, and bureaucracy that make entrepreneurship expensive and complicated. -/- This contradiction exposes a fundamental flaw in the capitalist model: pushing for population growth while failing to create enough sustainable jobs and businesses. -/- Sustainable Alternatives to the Pension System -/- Instead of relying on the outdated idea that pensions must be funded by an ever-growing workforce, societies can adopt new models that ensure financial security without economic manipulation: -/- 1. Universal Basic Income (UBI) -/- Governments could provide a fixed income to all citizens, eliminating the need for complex pension systems and reducing dependence on wage labor. This could be funded through controlled money creation and taxation of high-wealth individuals and corporations. -/- 2. Resource-Based Economy -/- Shifting from a money-driven system to a resource-based economy would prioritize efficient use of materials, automation, and sustainable development instead of profit-driven overproduction. -/- 3. Circular Economy -/- Instead of producing unnecessary products to create jobs, societies could focus on recycling, repair, and sustainability, creating long-term employment in industries that benefit humanity and the planet. -/- 4. Wealth Redistribution Policies -/- Implementing fair taxation on extreme wealth, financial transactions, and corporate profits would allow governments to fund pensions without economic instability. -/- Conclusion -/- The so-called pension crisis is not a natural consequence of aging populations but a deliberate economic strategy designed to maintain corporate power, suppress wages, and justify endless economic expansion. In a fiat currency system, governments have the power to fund pensions without relying on unsustainable population growth or debt-driven economies. However, they choose not to, prioritizing elite financial interests over public welfare. Instead of accepting this flawed model, societies should explore alternatives like universal basic income, sustainable economies, and fair wealth distribution to ensure a future where financial security is not dependent on artificial economic scarcity. -/- . (shrink)

This study explores the application of deep learning and image processing techniques for the detection of skin cancer. Leveraging convolutional neural networks (CNNs) and advanced image processing algorithms, the proposed system aims to accurately identify and classify skin lesions. The model is trained on a diverse dataset, encompassing various skin conditions, to enhance its diagnostic capabilities. Results demonstrate the potential for automated and reliable skin cancer detection, offering a promising approach for early diagnosis and improved patient outcomes. The deep learning (...) model is trained on a comprehensive dataset, including various types of skin lesions and conditions, to ensure robust performance across a spectrum of cases. Image preprocessing techniques are employed to enhance feature extraction and improve the model's ability to discern subtle patterns indicative of skin cancer. The study further investigates the interpretability of the deep learning model, employing techniques to visualize and understand the decision-making process. This transparency aids in building trust in the system's predictions and facilitates collaboration between AI and medical practitioners. As the landscape of healthcare continues to evolve, the combination of deep learning and image processing offers a scalable and efficient solution for skin cancer detection, fostering advancements in early intervention and personalized patient care. (shrink)

Technology is a practically indispensible means for satisfying one’s basic interests in all central areas of human life including nutrition, habitation, health care, entertainment, transportation, and social interaction. It is impossible for any one person, even a well-trained scientist or engineer, to know enough about how technology works in these different areas to make a calculated choice about whether to rely on the vast majority of the technologies she/he in fact relies upon. Yet, there are substantial risks, uncertainties, and unforeseen (...) practical consequences associated with the use of technological artifacts and systems. The salience of technological failure (both catastrophic and mundane), as well as technology’s sometimes unforeseeable influence on our behavior, makes it relevant to wonder whether we are really justified as individuals in our practical reliance on technology. Of course, even if we are not justified, we might nonetheless continue in our technological reliance, since the alternatives might not be attractive or feasible. In this chapter I argue that a conception of trust in technological artifacts and systems is plausible and helps us understand what is at stake philosophically in our reliance on technology. Such an account also helps us understand the relationship between trust and technological risk and the ethical obligations of those who design, manufacture, and deploy technological artifacts. (shrink)

As organizations increasingly adopt cloud computing to enhance scalability, efficiency, and costeffectiveness, securing cloud-based applications and data storage has become a paramount concern. This shift has redefined the role of network engineers, who are now at the forefront of implementing and managing secure cloud infrastructures. This research paper examines the critical responsibilities of network engineers in safeguarding cloud environments, focusing on the challenges, strategies, and tools they employ to mitigate risks and ensure data integrity. The paper identifies key challenges associated (...) with cloud security, including data breaches, misconfigured systems, insecure interfaces, and insider threats. These vulnerabilities are further exacerbated by the dynamic and decentralized nature of cloud ecosystems, which often involve multi-tenant architectures and complex integrations. The study underscores the need for network engineers to adapt to these complexities by acquiring specialized skills in cloud security protocols and emerging technologies. A significant focus is placed on the implementation of robust network security frameworks, such as zero-trust architecture, which emphasizes the principle of "never trust, always verify." Network engineers play a crucial role in deploying access control measures, encryption mechanisms, and intrusion detection systems to safeguard sensitive data. Additionally, the research highlights the importance of continuous monitoring and threat intelligence to identify and address vulnerabilities proactively. The integration of automation and artificial intelligence (AI) in cloud security is another critical area explored in this study. Automation tools enable network engineers to streamline routine tasks, such as patch management and configuration audits, thereby reducing human error and response time. AI-driven solutions, on the other hand, enhance threat detection capabilities by analyzing large datasets for anomalous patterns and potential attacks. The paper also discusses the collaborative responsibilities of network engineers, who must work closely with developers, cloud providers, and organizational stakeholders to align security measures with business objectives. This includes implementing secure DevOps practices, ensuring compliance with industry regulations, and conducting regular security audits. Case studies of successful cloud security implementations are presented to illustrate the effectiveness of these collaborative approaches. In conclusion, this research emphasizes the evolving role of network engineers as pivotal defenders of cloud-based systems. It calls for continuous professional development to stay abreast of emerging threats and technologies, as well as fostering a culture of shared responsibility for security within organizations. By adopting a proactive and comprehensive approach, network engineers can significantly enhance the resilience of cloud infrastructures, ensuring the confidentiality, integrity, and availability of critical data and applications. This study aims to provide actionable insights and a strategic framework for network engineers and organizations striving to navigate the complexities of securing cloud environments. Through a combination of technological expertise, innovative tools, and collaborative practices, network engineers are positioned to address the unique challenges posed by cloud computing and play a central role in shaping the future of secure digital ecosystems. (shrink)

: Ransomware remains one of the most significant cybersecurity threats, evolving rapidly with new attack vectors, encryption techniques, and extortion models. As we enter 2018, ransomware attacks have become more sophisticated, leveraging artificial intelligence (AI), automation, and emerging technologies to bypass traditional security measures. This paper analyzes the latest ransomware trends, including targeted attacks on critical infrastructure, Ransomware-as-a-Service (RaaS), and double/triple extortion tactics. Additionally, it explores advanced mitigation techniques such as AI-driven anomaly detection, zero-trust architectures, blockchain-based security solutions, (...) and proactive threat intelligence frameworks. By examining real-world case studies and industry best practices, this study provides insights into effective countermeasures and future directions for securing digital ecosystems against ransomware threats. The findings aim to assist cybersecurity professionals, policymakers, and organizations in strengthening their defense mechanisms against evolving ransomware threats in 2018 and beyond. (shrink)

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

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems have significantly diffused into various fields of our lives, serving as beneficial tools used by human agents. These systems are also evolving to act as co-assistants or semi-agents in specific domains, potentially influencing human thought, decision-making, and agency. Trust/distrust in AI plays the role of a regulator and (...) could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI. (shrink)

As organizations increasingly adopt cloud computing to enhance scalability, efficiency, and costeffectiveness, securing cloud-based applications and data storage has become a paramount concern. This shift has redefined the role of network engineers, who are now at the forefront of implementing and managing secure cloud infrastructures. This research paper examines the critical responsibilities of network engineers in safeguarding cloud environments, focusing on the challenges, strategies, and tools they employ to mitigate risks and ensure data integrity. The paper identifies key challenges associated (...) with cloud security, including data breaches, misconfigured systems, insecure interfaces, and insider threats. These vulnerabilities are further exacerbated by the dynamic and decentralized nature of cloud ecosystems, which often involve multi-tenant architectures and complex integrations. The study underscores the need for network engineers to adapt to these complexities by acquiring specialized skills in cloud security protocols and emerging technologies. A significant focus is placed on the implementation of robust network security frameworks, such as zero-trust architecture, which emphasizes the principle of "never trust, always verify." Network engineers play a crucial role in deploying access control measures, encryption mechanisms, and intrusion detection systems to safeguard sensitive data. Additionally, the research highlights the importance of continuous monitoring and threat intelligence to identify and address vulnerabilities proactively. The integration of automation and artificial intelligence (AI) in cloud security is another critical area explored in this study. Automation tools enable network engineers to streamline routine tasks, such as patch management and configuration audits, thereby reducing human error and response time. AI-driven solutions, on the other hand, enhance threat detection capabilities by analyzing large datasets for anomalous patterns and potential attacks. The paper also discusses the collaborative responsibilities of network engineers, who must work closely with developers, cloud providers, and organizational stakeholders to align security measures with business objectives. This includes implementing secure DevOps practices, ensuring compliance with industry regulations, and conducting regular security audits. Case studies of successful cloud security implementations are presented to illustrate the effectiveness of these collaborative approaches. In conclusion, this research emphasizes the evolving role of network engineers as pivotal defenders of cloud-based systems. It calls for continuous professional development to stay abreast of emerging threats and technologies, as well as fostering a culture of shared responsibility for security within organizations. By adopting a proactive and comprehensive approach, network engineers can significantly enhance the resilience of cloud infrastructures, ensuring the confidentiality, integrity, and availability of critical data and applications. This study aims to provide actionable insights and a strategic framework for network engineers and organizations striving to navigate the complexities of securing cloud environments. Through a combination of technological expertise, innovative tools, and collaborative practices, network engineers are positioned to address the unique challenges posed by cloud computing and play a central role in shaping the future of secure digital ecosystems. (shrink)

Our world is under going an enormous digital transformation. Nearly no area of our social, informational, political, economic, cultural, and biological spheres are left unchanged. What can philosophy contribute as we try to under- stand and think through these changes? How does digitization challenge past ideas of who we are and where we are headed? Where does it leave our ethical aspirations and cherished ideals of democracy, equality, privacy, trust, freedom, and social embeddedness? Who gets to decide, control, and (...) harness the powers of digitization and for which purposes? Epistemologically, do most of us understand these new mediations – and thus fabrics – of our new world? Lastly – how is the new technological landscape shaping not only our living conditions but also our collective imaginary and our self-identities? (shrink)

Purpose The purpose of this paper is to defend the notion of “trust in technology” against the philosophical view that this concept is misled and unsuitable for ethical evaluation. In contrast, it is shown that “trustworthy technology” addresses a critical societal need in the digital age as it is inclusive of IT-security risks not only from a technical but also from a public layperson perspective. Design/methodology/approach From an interdisciplinary perspective between philosophy andIT-security, the authors discuss a potential instantiation of (...) a “trustworthy information and communication technology ”: a solution for privacy respecting video surveillance. Here, strong data protection measures address grave concerns such as the threat of bulk biometric tracking of citizens. In a logical argument, however, the authors show that this technical notion of “trust” needs to be complemented by interlocking trust relations to justify public trust. Findings Based on this argument, the authors demonstrate that the philosophical position considering “trust in technology” to denote either “reliability” or “interpersonal trust” is too limited as it fails to address critical aspects of IT-security. In a broader, socio-technical sense, however, it is shown that several distinct accounts of trust – technical, interpersonal and institutional – should meaningfully interlock, to address concerns with ICTs. Originality/value This conceptual study demonstrates the potential of “trust in technology” for a more comprehensive evaluation of ICTs within the context of operation. Furthermore, it adds to the discussion of trust in IT-security by highlighting the layperson’s challenge of judging a technology’s trustworthiness. Vice versa, it contributes to Ethics of Technology by highlighting crucial IT-security needs. (shrink)

In this chapter, we consider ethical and philosophical aspects of trust in the practice of medicine. We focus on trust within the patient-physician relationship, trust and professionalism, and trust in Western (allopathic) institutions of medicine and medical research. Philosophical approaches to trust contain important insights into medicine as an ethical and social practice. In what follows we explain several philosophical approaches and discuss their strengths and weaknesses in this context. We also highlight some relevant empirical (...) work in the section on trust in the institutions of medicine. It is hoped that the approaches discussed here can be extended to nursing and other topics in the philosophy of medicine. (shrink)

Until recently, psychologists have conceptualised and studied trust in God (TIG) largely in isolation from contemporary work in theology, philosophy, history, and biblical studies that has examined the topic with increasing clarity. In this article, we first review the primary ways that psychologists have conceptualised and measured TIG. Then, we draw on conceptualizations of TIG outside the psychology of religion to provide a conceptual map for how TIG might be related to theorised predictors and outcomes. Finally, we provide a (...) research agenda for future empirical work in this area, as well as practical applications for counsellors and religious leaders. (shrink)

Most traditional epistemologists see trust and knowledge as deeply antithetical: we cannot know by trusting in the opinions of others; knowledge must be based on evidence, not mere trust. I argue that this is badly mistaken. Modern knowers cannot be independent and self-reliant. In most disciplines, those who do not trust cannot know. Trust is thus often more epistemically basic than empirical evidence or logical argument, for the evidence and the argument are available only through (...) class='Hi'>trust. Finally, since the reliability of testimonial evidence depends on the trustworthiness of the testifier, this implies that knowledge often rests on a foundation of ethics. The rationality of many of our beliefs depends not only on our own character, but on the character of others. (shrink)

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

Health care systems can positively influence our personal decision-making and health-related behavior only if we trust them. I propose a conceptual analysis of the trust relation between the public and a healthcare system, drawing from healthcare studies and philosophical proposals. In my account, the trust relation is based on an epistemic component, epistemic authority, and on a value component, the benevolence of the healthcare system. I argue that it is also modified by the vulnerability of the public (...) on healthcare matters, and by the system’s credibility. I apply my proposed analysis of public trust in health care systems to the phenomenon of vaccine hesitancy, a tendency to question vaccine policies, and to seek alternative vaccine schedules or to refuse vaccination. Understanding the role of trust and its components can be key to understanding the phenomenon. (shrink)

What makes science trustworthy to the public? This chapter examines one proposed answer: the trustworthiness of science is based at least in part on its independence from the idiosyncratic values, interests, and ideas of individual scientists. That is, science is trustworthy to the extent that following the scientific process would result in the same conclusions, regardless of the particular scientists involved. We analyze this "idiosyncrasy-free ideal" for science by looking at philosophical debates about inductive risk, focusing on two recent proposals (...) which offer different methods of avoiding idiosyncrasy: the high epistemic standards proposal and the democratic values proposal. (shrink)

Paolo Freire argued that trust is essential to what he called the “problem-posing” model of education. This chapter builds on that insight and explores different ways that trust plays out in the classroom, focusing on three different types. The first type of trust is from teacher to student – trusting that students will show up prepared and ready to do the work together. The second type of trust is from student to teacher – trusting that the (...) method and design of the course (from the assignment structure to story arc of the topics and texts) will be coherent and worthwhile. The final type of trust is that which emerges holistically, in which what Freire called the teacher-student and student-teachers build a trustful epistemic community together. (shrink)

Collaborative filtering is being used within organizations and in community contexts for knowledge management and decision support as well as the facilitation of interactions among individuals. This article analyzes rhetorical and technical efforts to establish trust in the constructions of individual opinions, reputations, and tastes provided by these systems. These initiatives have some important parallels with early efforts to support quantitative opinion polling and construct the notion of “public opinion.” The article explores specific ways to increase trust in (...) these systems, albeit a “guarded trust” in which individuals actively seek information about system foibles and analyze the reputations of participants. (shrink)

Data from the World Values Survey shows that generalized trust in Mainland China—trust in out-group members—is very low, but generalized trust in Taiwan is much higher. The present article argues that positive interactions with out-group members in the context of Taiwan’s export-oriented economy fostered generalized trust—and so explains this difference. This line of argument provides evidence for Albert O. Hirschman’s doux commerce thesis, that market interaction can improve persons and even stabilize the social order. The present (...) article defends this point by separating two theses that Hirschman combines under that label, a countervailing forces thesis and a doux commerce thesis narrowly understood. These theses offer different explanations (or mechanisms) for how commerce could have those positive effects. The data about Taiwanese trust practices provides evidence for the latter. (shrink)

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since (...) AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice. (shrink)

We provide an analysis of the public's having warranted epistemic trust in science, that is, the conditions under which the public may be said to have well-placed trust in the scientists as providers of information. We distinguish between basic and enhanced epistemic trust in science and provide necessary conditions for both. We then present the controversy regarding the connection between autism and measles–mumps–rubella vaccination as a case study to illustrate our analysis. The realization of warranted epistemic public (...) trust in science requires various societal conditions, which we briefly introduce in the concluding section. (shrink)

In this paper, I distinguish three general approaches to public trust in science, which I call the individual approach, the semi-social approach, and the social approach, and critically examine their proposed solutions to what I call the problem of harmful distrust. I argue that, despite their differences, the individual and the semi-social approaches see the solution to the problem of harmful distrust as consisting primarily in trying to persuade individual citizens to trust science and that both approaches face (...) two general problems, which I call the problem of overidealizing science and the problem of overburdening citizens. I then argue that in order to avoid these problems we need to embrace a (thoroughly) social approach to public trust in science, which emphasizes the social dimensions of the reception, transmission, and uptake of scientific knowledge in society and the ways in which social forces influence both positively and negatively the trustworthiness of science. (shrink)

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

There is a growing consensus among philosophers of science that core parts of the scientific process involve non-epistemic values. This undermines the traditional foundation for public trust in science. In this article I consider two proposals for justifying public trust in value-laden science. According to the first, scientists can promote trust by being transparent about their value choices. On the second, trust requires that the values of a scientist align with the values of an individual member (...) of the public. I argue that neither of these proposals work and suggest an alternative that does better. When scientists must appeal to values in the course of their research, they should appeal to democratic values: the values of the public or its representatives. (shrink)

Trust is important in the food sector. This is primarily because households entrust some of the tasks related to food preparation to food processors. The public is concerned about pesticides, food additives, preservatives, and processed foods that may harbor unwanted chemicals or additives. After numerous food scandals, consumers expect food processing industries and retailers to take responsibility for food safety. Meanwhile, the food industry focuses on profit growth and costs reduction to achieve higher production efficiency and competitiveness. It means (...) that they introduce innovations, such as new production methods, processing techniques, and additives. Consumers have to delegate the responsibility for ensuring food safety to food producers, retailers, and regulatory authorities who ensure that the foods are safe, healthy, and pose no risks. For consumers, trusting these actors can reduce feelings of uncertainty. It is helpful for companies to be responsible for their activities through transparency and traceability. In turn, the food industry tries to gain consumers’ trust by providing objective information, such as ranked brands or labels on food packages. (shrink)

It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the (...) functional role of trust changes across contexts and objects is further complicated when communities and individuals mediate it through technologies, and even more so when that mediation involves artificial intelligence (AI) and machine learning (ML). In this chapter I look at the ways in which trust in institutions, and specifically the medical profession, is affected by the use of AI and ML. There are two key elements of this analysis. The first is a disanalogy between institutional trust in medicine and institutional trust in science (Irzik and Kurtulmus 2021, 2019; Kitcher 2001). I note that as AI and ML become a more prominent part of medicine, trust in a medical institution becomes more like trust in a scientific institution. This is problematic for institutional trust in medicine and the practice of medicine, since institutional trust in science has been undermined by, among other things, the spread of misinformation online and the replication crisis (Romero 2019). There is also a strong analogy between the psychological state of the person who trusts a scientific report or testimony and the psychological state of a patient who trusts individual recommendations made by a medical professional in a clinical setting. In both cases, institutional trust makes it less likely that a mistake or malfeasance will result in reactive attitudes, such as blame or anger, directed at other individual members of that institution. However, it also renders people vulnerable enough to blame the institution itself. This, with time, can erode trust in the institution and naturally leads to policy recommendations that aim to preserve institutional trust. I survey two ways in which that can be done with institutional trust in medicine in the age of AI and ML. (shrink)

Expert knowledge regularly informs personal and civic-decision making. To decide which experts to trust, lay publics —including policymakers and experts from other domains—use different epistemic and non-epistemic cues. Epistemic cues such as honesty, like when experts are forthcoming about conflicts of interest, are a popular way of understanding how people evaluate and decide which experts to trust. However, many other epistemic cues, like the evidence supporting information from experts, are inaccessible to lay publics. Therefore, lay publics simultaneously use (...) second-order social cues in their environment to inform decisions to trust. These second-order social cues, or ‘social indicators of trust’, prevent lay publics from having to trust blindly. Social indicators of trust therefore inform lay publics’ epistemic vigilance, or constant low level-monitoring of testimony from experts. This special issue examines the nature, acquisition and application of social indicators of trust for scientific experts and institutions. It also raises questions about the types of trust asked of lay publics and challenges traditional normative assumptions about the relationship between science and lay publics through study of attitudes, values, and experiences. The issue descriptively re-examines the structure of institutions, their role and methods for ferrying information, as well as how social indicators operate in times of crisis. In this collection of works, we bridge history, science, philosophy of science, science and technology studies, science communication and social epistemology, to broaden the discourse on trust in experts and more accurately reflect the imperfect yet indispensable endeavour that trusting is. (shrink)

Sparrow argues that military robots capable of making their own decisions would be independent enough to allow us denial for their actions, yet too unlike us to be the targets of meaningful blame or praise—thereby fostering what Matthias has dubbed “the responsibility gap.” We agree with Sparrow that someone must be held responsible for all actions taken in a military conflict. That said, we think Sparrow overlooks the possibility of what we term “blank check” responsibility: A person of sufficiently high (...) standing could accept responsibility for the actions of autonomous robotic devices—even if that person could not be causally linked to those actions besides this prior agreement. The basic intuition behind our proposal is that humans can impute relations even when no other form of contact can be established. The missed alternative we want to highlight, then, would consist in an exchange: Social prestige in the occupation of a given office would come at the price of signing away part of one's freedoms to a contingent and unpredictable future guided by another agency. (shrink)

Deepfakes pose a multi-faceted threat to the acquisition of knowledge. It is widely hoped that technological solutions—in the form of artificially intelligent systems for detecting deepfakes—will help to address this threat. I argue that the prospects for purely technological solutions to the problem of deepfakes are dim. Especially given the evolving nature of the threat, technological solutions cannot be expected to prevent deception at the hands of deepfakes, or to preserve the authority of video footage. Moreover, the success of such (...) technologies depends on institutional trust that is in short supply. Finally, outsourcing the discrimination between the real and the fake to automated, largely opaque systems runs the risk of undermining epistemic autonomy. (shrink)

It is easier to talk frankly to a person whom one trusts. It is also easier to agree with a scientist whom one trusts. Even though in both cases the psychological state that underlies the behavior is called ‘trust’, it is controversial whether it is a token of the same psychological type. Trust can serve an affective, epistemic, or other social function, and comes to interact with other psychological states in a variety of ways. The way that the (...) functional role of trust changes across contexts and objects is further complicated when communities and individuals mediate it through technologies, and even more so when that mediation involves artificial intelligence (AI) and machine learning (ML). In this chapter I look at the ways in which trust in institutions, and specifically the medical profession, is affected by the use of AI and ML. There are two key elements of this analysis. The first is a disanalogy between institutional trust in medicine and institutional trust in science (Irzik and Kurtulmus 2021, 2019; Kitcher 2001). I note that as AI and ML become a more prominent part of medicine, trust in a medical institution becomes more like trust in a scientific institution. This is problematic for institutional trust in medicine and the practice of medicine, since institutional trust in science has been undermined by, among other things, the spread of misinformation online and the replication crisis (Romero 2019). There is also a strong analogy between the psychological state of the person who trusts a scientific report or testimony and the psychological state of a patient who trusts individual recommendations made by a medical professional in a clinical setting. In both cases, institutional trust makes it less likely that a mistake or malfeasance will result in reactive attitudes, such as blame or anger, directed at other individual members of that institution. However, it also renders people vulnerable enough to blame the institution itself. This, with time, can erode trust in the institution and naturally leads to policy recommendations that aim to preserve institutional trust. I survey two ways in which that can be done with institutional trust in medicine in the age of AI and ML. (shrink)

The paper examines the ways trust can be re-build for the future of free trade.

In lieu of an abstract, here is a brief excerpt of the content:Journal of the History of Ideas 61.3 (2000) 453-471 [Access article in PDF] Liberty, Authority, and Trust in Burke's Idea of Empire Richard Bourke When Edmund Burke first embarked upon a parliamentary career, British political life was in the process of adapting to a series of critical reorientations in both the dynamics of party affiliation and the direction of imperial policy. During the period of the Seven Years' (...) War, a reconstituted militia became a focus for patriotic enthusiasm, uniting national sentiment against France and effectively eradicating the remnants of Jacobite rhetoric and aspiration. Traditional opposition between Tory and Whig became irrelevant, while Court and Country jointly came to the support of the Pitt-Newcastle ministry. However, in due course the Old Corps disintegrated as George III succeeded to the throne with the promise of an end to factional strife and the beginning of a patriotic alliance in government. But while competing ideologies and interests on the domestic political scene were undergoing comprehensive realignment, the pursuit of a blue-water policy in tandem with strategic continental campaigns brought Britain's struggle against France into North America, the Caribbean, and India. Trade continued to expand into Asia and West Africa, but the principles of commercial advantage were continually brought into open conflict with the exigencies of war. The division of power in Europe became embroiled in a substantial redivision of empire, and by 1763 British territorial expansion had reached its zenith.When Burke was appointed private secretary to the Marquis of Rockingham, with the national debt standing at an all time high and the demands of imperial defense unlikely to diminish, the politics of empire called for urgent attention from all sections of the political establishment. In particular the Rockinghamites, adopting a posture of aloofness from venality and petty interest, were obliged to advance policies for imperial government on the solid basis of political principle. 1 Coming into parliament as a member for Wendover in the wake of [End Page 453] Grenville's Stamp Act, at a time when the East India Company was assuming control of the revenue in Bengal, Burke set about formulating a doctrine of imperial sovereignty which would be serviceable to the party either in power or in opposition and which would be adequate to the complex reality of detached and extensive empire. The immediate reception of that inquiry, both in Britain and on the Continent, into the fraught world of post-Revolutionary political debate has had the long-term effect of distorting the order of emphasis in terms of which his career as a whole ought properly to be understood.The vigorous elaboration, in opposition to the Jacobin experiment in France, of the conditions under which moderate government could be exercised within the context of an absolute sovereignty was expressed in terms which had originally been invoked as part of a sustained attempt to reconcile civility with empire. As we shall see, that attempt involved a process of delicate coordination by means of which the interests of freedom would be brought into harmony with the demands of public power. In that sense the effort to make liberty and authority mutually responsible can be said to constitute the core ambition of Burke's political rhetoric. In this context responsibility defines the optimally beneficial relation between government and people on the one hand and national sovereignty and the extended empire on the other. This article outlines the practical and theoretical exploration of those relations within the frame of Burke's intervention in the world of eighteenth-century political discourse.Burke was of course well placed in 1765 to evolve a theory of imperial government that was at once ideologically compelling and politically viable. In his capacity as secretary to William Gerard Hamilton in the early 1760s he had already examined the corruption of legitimate empire as represented by the introduction of penal legislation into Ireland after 1694. The Tracts Relating to Popery Laws set out to convict the Irish Parliament in Dublin of flagrant misrule. A subordinate legislature within the empire is charged with having employed... (shrink)

Abstract: Adoption of digital system becomes an innovative revolution to replace the tradition work into scientific means. Electronic government is the use of technology in which particular wireless based application sustains the provision of online interconnectivity in the way to convey a speedy service delivery and materialize an easy accessibility of users via government portal. Moreover, the vitality of e-government has been recognized in the contemporary era of science and technology across the world. The chief aim of this study to (...) represent a literature of an electronic government use in a conceptual means in which proposed determinants as PEOU, PU, TIEG, SI, and ITU envisage the intention of the users’ online services of government in a Pakistan perspective. In the existing literature all constructs of the proposed model possess an effect on the intention to use e - government and the theoretical approach encompasses the TAM and UTAUT model to assist the technological adopti on of the employee's use of the innovative system in an organization. However, Pakistan has been ranked low in the e-government development index that seems the backwardness of the e-government adaptation in the country that is recognized as the dire need to be adjusted with public sectors to sustain an effective and efficient service delivery. It seems the poor literacy rate an d financial constraints are serious obstacles for adopting e-government technology in the working atmosphere of the diverse public sector organizations. Keywords: Electronic government, Perceived usefulness, Obstacles, Recommendations, Intention to use. (shrink)

In the 21st century, transparency and efficiency in municipal management are essential. This study focusses on the GAD Municipal of Latacunga, Ecuador, assessing inefficiency in the Office of Valuation and Cadastres, proposing the use of AI to improve governance. The research reveals deficiencies in digitalization and transparency, identifying the need for effective mechanisms to optimize public services. AI is presented as a potential solution to automate tasks, detect irregularities, and increase administrative efficiency. This analysis, based on regional and local data, (...) highlights the potential of AI to transform public administration and strengthening citizen trust. (shrink)

The ongoing explosion of interest in artificial intelligence is fueled in part by recently developed techniques in machine learning. Those techniques allow automated systems to process huge amounts of data, utilizing mathematical methods that depart from traditional statistical approaches, and resulting in impressive advancements in our ability to make predictions and uncover correlations across a host of interesting domains. But as is now widely discussed, the way that those systems arrive at their outputs is often opaque, even to the experts (...) who design and deploy them. Is it morally problematic to make use of opaque automated methods when making high-stakes decisions, like whether to issue a loan to an applicant, or whether to approve a parole request? Many scholars answer in the affirmative. However, there is no widely accepted explanation for why transparent systems are morally preferable to opaque systems. We argue that the use of automated decision-making systems sometimes violates duties of consideration that are owed by decision-makers to decision-subjects, duties that are both epistemic and practical in character. Violations of that kind generate a weighty consideration against the use of opaque decision systems. In the course of defending our approach, we show that it is able to address three major challenges sometimes leveled against attempts to defend the moral import of transparency in automated decision-making. (shrink)

In today’s fast moving insurance environment, efficient claim management is a vital business practice to improve operational effectiveness and provide an exceptional customer experience. Based on the first notice of loss (FNOL), Guidewire Claim Center, the leading claims management platform, gives insurers the thorough, scalable, and customizable option to accelerate the claims process from FNOL to final resolution. In this paper, we explore how insurers adapt Guidewire Claim Center to enable effective utilization of Guidewire Claim Center to optimize and transform (...) their claim management process, making better decisions, and delivering better customer experience. Advanced automation features, their role in reducing manual footprint, and the integration of the platform with external systems for a smooth data flow and analytics driven insights are key things covered. Finally we see real world case studies detailing where Guidewire's predictive analytics are in use for fraud detection, expedited claims processing and building proactive relationships between the claimant and the Carrier. We also look at best practices for implementation and ways to work around typical problems, including change management, system customization, and others. Framed around the dual aims of operational efficiency and customer satisfaction, this blueprint offers practical advice for insurers navigating a more and more digitalized industry. Guidewire Claim Center is agile, flexible foundations that can help insurers transform their claims operations, while also creating lasting trust and loyalty with policyholders. (shrink)

Purpose – The internet creates ample opportunities to start a mobile social commerce business. The literature confirms the issue of customer trust for social commerce businesses is a challenge that must be addressed. Hence, this study aims to examine the antecedents of trust in mobile social commerce by applying linear and non-linear relationships based on partial least squares structural equation modeling and an artificial neural network model. -/- Design/methodology/approach – This study applied a non-linear artificial neural network approach (...) to provide a further understanding of the determinants of trust in mobile social commerce based on a non-linear and non-compensatory model. Besides, a questionnaire was distributed to 340 social commerce customers in Malaysia. -/- Findings – The conceptual framework for investigating trust in mobile social commerce has various advantages and contributions to predicting consumer behaviour. The results of the study showed there is a positive and significant relationship between social support, presence and unified theory of acceptance and use of technology2 (UTAUT2). In addition, UTAUT2 has fully mediated the relationship between social support, presence and trust in social commerce. Finally, the results concluded the relationship between UTAUT2 and trust in social commerce would be stronger when the diffusion of innovation and innovation resistance is high and low, respectively. -/- Research limitations/implications – The current study provides a novel perspective on how customers can trust social m-commerce to provide real solutions to managers of encouraging e-marketing among consumers. -/- Practical implications – This paper shows how businesses can develop trust in social m-commerce in Malaysian markets. The findings of this study probably could be extended to other businesses in Asia or other countries. Because trust in social e-commerce has a dynamic role in consumer behaviour and intention to purchase. -/- Originality/value – This study provided a new perspective on mobile social commerce and paid more attention to an investigation of such emerging commerce. The originality of this study is embodied by investigating an integrated model that included different theories that presented new directions of trust in mobile social commerce through social and behavioural determinants. (shrink)

A recent report revealed a staggering 61% drop in the market value of carbon offsets, attributed to adverse findings from both the scientific community and the media. These findings have raised concerns about the effectiveness of carbon credit projects, leading investors to reduce their involvement or withdraw completely, thereby substantially decreasing the market’s overall value [1,2]. This reluctance among buyers is fueled by escalating skepticism about the actual environmental impact of these projects and concerns over potential “greenwashing,” where companies misleadingly (...) present their environmental practices as more eco-friendly than they are. (shrink)

Trust has a great potential for furthering our understanding of organizational change and learning. This potential however remains largely untapped. It is argued that two reasons as for why this potential remains unrealized are: (i) A narrow conceptualization of change as implementation and (ii) an emphasis on direct and aggregated effects of individual trust to the exclusion of other effects. It is further suggested that our understanding of the effects of trust on organizational change, should benefit from (...) including effects of trust on the formulation stage. It should also benefit from exploring the structuring effects of trust in organizations. Throughout this chapter, ways to extend current research on trust in organizations are suggested. The chapter also provides examples of relevant contributions where available. In order to capture organizational effects of trust, it is suggested that trust should be studied over longer time intervals, and include several referents of trust, spanning both horizontal and vertical relationships in the organization. (shrink)