TL;DR
The study presents the T-STAEF (Temporal Socio-Technical AI Ethics Framework) to provide a temporal socio-technical lens to view the ethical management of AI systems and provides structural, conceptual model to assist organisations in understanding why there are consistent ethical issues related to AI.
A Multi-Layer Social-Theoretical AI Ethics Framework (MLST-AEF) that integrates normative ethical reasoning, stakeholder analysis, institutional context, bias and power assessment, and structured decision support is developed.
Recent years have seen a growing discrepancy in the field of AI alignment: research and policy recommendations on AI ethics tend to assume a general set of ethical values, yet proliferating practice-specific uses of AI systems on the ground - in the legal, medical and translation domains, among others - have been effectively manifesting ethics of professional practice. This article begins by outlining the reasons why general and professional ethics are increasingly conflicted in contemporary AI systems, and by surveying how the research literature attests to, but has not yet resolved, this conceptual and practical challenge. We then conceptualize the main dimensions of AI models'decision-making in areas of professional practice, emphasizing professional ethics'hierarchically structured relationship with general ethics, and elaborating on the mechanisms through which they reach an equilibrium in situational contexts that involve conflict. It is through this equilibrium, we suggest, that certain professional ethics are prioritized over others and implemented in practice. We then show how our framework can be the basis for a systematic empirical assessment of AI models'professional ethics in various domains, identifying the nuances of the models'favored ethic by examining their production in a series of similar but not identical scenarios. Finally, we propose a formulation for how to intervene in and change AI models'favored ethics in professional practices - while noting the inherent dimension of subjectivity involved in both the evaluation and implementation of professional ethics in AI models.
The most influential factor in ethical AI development is “Autonomy and human bias,” followed by “Intentionality and responsibility,” followed by “Intentionality and responsibility”; the “Automation and replacement” factor was ranked the least influential.
Fletcher’s agapeic framework may solve epistemic inequities in global AI discourse and fosters inclusive technology growth by fusing situational ethics with traditional African philosophies.
In order to live well in an AI-based society, designing technically ethical systems, in the spirit of consequentialism, is not enough; rather, it is essential to cultivate citizens who, as AI users, are endowed with virtues and, first and foremost, the virtue of prudence.
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