Towards friendly AI: a comprehensive review and new perspectives on human-AI alignment

Abstract As Artificial Intelligence (AI) continues to advance rapidly, Friendly Artificial Intelligence (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 technical subfields 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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Publication Details

Journal
AI and Ethics
Published
2026-03-08
DOI
https://doi.org/10.1007/s43681-026-01070-x
Primary Topic
Explainable Artificial Intelligence (XAI)
Type
article
Field-Weighted Citation Impact
0.00
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article

Towards friendly AI: a comprehensive review and new perspectives on human-AI alignment

Bjoern W. Schuller, Sunil Munthumoduku Krishna Murthy, Qiyang Sun, Emran Alturki et al.
AI and Ethics
Explainable Artificial Intelligence (XAI)
article

Towards friendly AI: a comprehensive review and new perspectives on human-AI alignment

Bjoern W. Schuller, Sunil Munthumoduku Krishna Murthy, Qiyang Sun, Emran Alturki, Yupei Li
article en

Abstract

Abstract As Artificial Intelligence (AI) continues to advance rapidly, Friendly Artificial Intelligence (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 technical subfields 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.

AI and EthicsVol. 6(2)
Imperial College London (GB), Technical University of Munich (DE)
Partnerships for the goals
Openalex Percentile: Top 35%
Explainable Artificial Intelligence (XAI)
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