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Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment

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arxiv 2412.15114 v1 pith:NSA67NWO submitted 2024-12-19 cs.AI cs.CY

Qiyang Sun , Yupei Li , Emran Alturki , Sunil Munthumoduku Krishna Murthy , Björn W. Schuller This is my paper · ORCID
classification cs.AIcs.CY
keywords developmentperspectivesadvocateapplicationscomprehensiveethicalfriendlyfuture
verification ladder T0 review T1 audit T2 compute T3 formal
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As Artificial Intelligence (AI) continues to advance rapidly, Friendly AI (FAI) has been proposed to advocate for more equitable and fair development of AI. Despite its importance, there is a lack of comprehensive reviews examining FAI from an ethical perspective, as well as limited discussion on its potential applications and future directions. This paper addresses these gaps by providing a thorough review of FAI, focusing on theoretical perspectives both for and against its development, and presenting a formal definition in a clear and accessible format. Key applications are discussed from the perspectives of eXplainable AI (XAI), privacy, fairness and affective computing (AC). Additionally, the paper identifies challenges in current technological advancements and explores future research avenues. The findings emphasise the significance of developing FAI and advocate for its continued advancement to ensure ethical and beneficial AI development.

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Cited by 2 Pith papers

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  1. Human-Centered AI Communication in Co-Creativity: An Initial Framework and Insights

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  2. Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives

    cs.AI 2025-06 conditional novelty 4.0 of 10

    A survey proposes a macro-meso-micro value framework for agentic AI alignment and maps applications, methods, and benchmarks onto it.

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