TL;DR
This study’s findings suggest that paradoxical tendencies found in individual consumer behavior exist in institutional behavior, as there was no relationship between ethical beliefs among AI professionals and data practices in their AI designs.
AI has quickly ceased being a supporting computational device and has become a central decision-maker in various spheres of society, such as health care, finances, criminal justice, and government, education, and employment. Decision-making systems based on AI have an increasing impact on the outcomes that have significant ethical, legal, and social implications on society and individuals. Although these systems have efficacy, scalability, and objectiveness, they also introduce some essential ethical dilemmas associated with bias, fairness, transparency, accountability, privacy, autonomy, and social justice. The paper will be a systematic review of the ethical issues of AI-controlled decision-making in the society. The investigation is an interdisciplinary synthesis of the literature in the domain of computer science, philosophy, law, and social sciences in order to distinguish the major ethical hazards and novel normative structures. It analyzes the origin of algorithmic bias in data, structural model design and institutional conditions and how explainability and trust are endangered through the lack of transparency in complex machine learning models. Specific focus is made on the asymmetries of power that the AI implementation causes in which automated systems impact vulnerable and marginalized people unequally. It is suggested in the paper that a methodology of ethical governance based on principles of responsible AI should be structured, fairness-by-design, transparency, human-in-the-loop oversight, and constant impact assessment. The conceptual model of ethical risk assessment is presented to consider AI systems throughout its lifecycle, including data collection and after-deployment language. The findings underscore the fact that AI systems have ethical failures that are seldom technical but rather socio-technical, which necessitate interventions at the policy, organizational governance, and technical design levels. The paper highlights the necessity of ethical norms that are enforceable, interdisciplinary cooperation, and international harmonization of regulations to make sure that the decisions made by AI could be consistent with the basic human values. The paper ends by identifying the future research directions/decision-making, as well as determining the policy implications of transforming AI systems into trustworthy, accountable, and socially beneficial systems.
It is concluded that RAI is not merely a compliance burden but the core enabling infrastructure for Industry 5.0, with its successful implementation dependent on a symbiotic fusion of policy, technology, and organizational governance.
Abstract The integration of Artificial Intelligence (AI) into competition authorities to detect anti-competitive practices entails inherent ethical risks, such as algorithmic bias and excessive dependence on technology providers. To mitigate these risks, this article investigates how thirty-five regulatory authorities, ranked in the 2023 GCR Enforcement Rating, address these challenges. A document analysis, carried out using ATLAS.ti25, compares these organizations based on five ethical principles (transparency, accountability, fairness and equity, robustness and security, and privacy), grounded in consolidated frameworks in AI ethics and protection bioethics. The results reveal a critical disparity in regulatory maturity: greater technical rigor is observed in operational ethics principles (robustness, privacy, and security) than in social ethics principles. Substantial deficiencies persist in transparency, accountability, and fairness/equity. This gap points to a deficit in the core principle of explainability (encompassing both intelligibility and accountability). The main cause lies in the absence of clear procedures and limited disclosure regarding the use of AI in the core activities of these organizations. The study concludes that strengthening ethical leadership and establishing organizational accountability mechanisms are essential to ensure the fair and transparent application of AI in economic regulation.
Results show that AI complements HRM, but ethics must be taken into consideration for responsible and sustainable labour management, but ethics must be taken into consideration for responsible and sustainable labour management.
Consent in data protection law is highly contentious. Critics argue that enabling people to make their own decisions is not feasible, as people are generally poor decision-makers. However, proponents insist on the value of consent as a tool of empowerment. This Article challenges the foundational assumptions of this debate: that consent is conducive to privacy; that the decision to share one’s data concerns mainly the person sharing them; and that if we were to bridge the gap between the layperson and the ideal decision-maker, we would achieve an optimal level of data privacy protection. Setting aside these false assumptions, this Article aims to resituate the consent debate in a framework that rests on more solid theoretical ground. First, it connects the economic concept of data externalities with the philosophical idea of the harm principle. This highlights how unilaterally imposing burdens on others, without consideration of their interests, is morally unjustifiable, thereby depriving consent of its normative justification. Second, it examines privacy as a public good, demonstrating that even rational decision-makers are bound to freeride on each other’s data privacy and make everyone worse-off, thereby rendering consent undesirable. Therefore, this Article concludes that we should abolish consent, while retaining individual control over data through data privacy rights.
