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

MCML Authors

Björn Schuller

Prof. Dr.

Principal Investigator

Health Informatics

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.

misc

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Preprint

Dec. 2024

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Authors

Q. Sun • Y. Li • E. Alturki • S. M. K. Murthy • B. W. Schuller

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Links

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Research Area

 B3 | Multimodal Perception