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Building Ethics into Artificial Intelligence

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arxiv 1812.02953 v1 pith:SSLR7FS5 submitted 2018-12-07 cs.AI

Han Yu , Zhiqi Shen , Chunyan Miao , Cyril Leung , Victor R. Lesser , Qiang Yang This is my paper · ORCID
classification cs.AI
keywords ethicaltopicartificialdecisionethicsframeworksgovernanceintelligence
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Toward Virtuous Reinforcement Learning: A Critique and Roadmap

    cs.AI 2025-12 unverdicted novelty 5.0 of 10

    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, regularizat...

  2. HKGAI-V1: Towards Regional Sovereign Large Language Model for Hong Kong

    cs.CL 2025-07 reject novelty 5.0 of 10

    A DeepSeek-based model fine-tuned for Hong Kong outperforms general models on Hong Kong benchmarks, but most of those benchmarks are self-authored and unreleased.

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