The paper argues for modeling ethics in RL as relatively stable habits and dispositions rather than rules or scalar rewards, and provides a four-part roadmap using social learning, multi-objective methods, regularization, and diverse ethical traditions.
Building Ethics into Artificial Intelligence
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abstract
As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.
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cs.AI 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
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Toward Virtuous Reinforcement Learning: A Critique and Roadmap
The paper argues for modeling ethics in RL as relatively stable habits and dispositions rather than rules or scalar rewards, and provides a four-part roadmap using social learning, multi-objective methods, regularization, and diverse ethical traditions.