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

• URL: http://arxiv.org/abs/2412.15114v1
• Date: Thu, 19 Dec 2024 17:56:08 GMT
• Title: Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment
• Authors: Qiyang Sun, Yupei Li, Emran Alturki, Sunil Munthumoduku Krishna Murthy, Björn W. Schuller,
• Abstract summary: Friendly AI (FAI) has been proposed to advocate for more equitable and fair development of AI. This paper provides a thorough review of FAI, focusing on theoretical perspectives both for and against its development. Key applications are discussed from the perspectives of XAI, privacy, fairness and affective computing.
• Score: 35.63053777817013
• License:
• Abstract: 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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