AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
The analysis indicates that the pragmatist narrative of the sociotechnical systems approach emphasizes the real-world functioning of AI and its societal impacts.
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.
The main dimensions of AI models'decision-making in areas of professional practice are conceptualized, 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.
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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