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

Qiyang Sun, Yupei Li, Emran Alturki, Sunil Munthumoduku Krishna Murthy, Björn W. Schuller

TL;DR

This paper addresses the lack of comprehensive reviews on Friendly AI (FAI) by offering a systematic synthesis of ethical perspectives, a formal definition, and an organizational map of related technologies. It analyzes both supporters' frameworks (value alignment, deontology, altruism, corrigibility, and $CEV$) and critical challenges (moral, technical, safety, and compliance) while connecting FAI to practical domains such as Explainable AI, privacy, fairness, and affective computing. The work clarifies how XAI, privacy-preserving methods, fairness mechanisms, and affective computing can operationalize FAI in current ANI systems and outlines future research directions, including unified definitions, cross-cultural ethics, and multi-stakeholder collaboration. Its contributions aim to guide researchers, policymakers, and practitioners toward responsible, values-aligned AI as the field advances toward AGI/ASI.

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.

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

TL;DR

This paper addresses the lack of comprehensive reviews on Friendly AI (FAI) by offering a systematic synthesis of ethical perspectives, a formal definition, and an organizational map of related technologies. It analyzes both supporters' frameworks (value alignment, deontology, altruism, corrigibility, and ) and critical challenges (moral, technical, safety, and compliance) while connecting FAI to practical domains such as Explainable AI, privacy, fairness, and affective computing. The work clarifies how XAI, privacy-preserving methods, fairness mechanisms, and affective computing can operationalize FAI in current ANI systems and outlines future research directions, including unified definitions, cross-cultural ethics, and multi-stakeholder collaboration. Its contributions aim to guide researchers, policymakers, and practitioners toward responsible, values-aligned AI as the field advances toward AGI/ASI.

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.

Paper Structure

This paper contains 45 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Friendly AI definition
  3. Theoretical Perspectives
  4. Support Side
  5. Cornerstone
  6. Value Alignment
  7. Deontology
  8. Altruism
  9. Opposition Side
  10. Moral and Technical Challenges
  11. Ambiguity and Evolution
  12. Safety and Trust Risk
  13. Evaluation and Compliance Issues
  14. Applications
  15. Explainable AI
  16. ...and 30 more sections

Figures (3)

  • Figure 1: Theoretical framework of FAI.
  • Figure 2: Stages of AI development
  • Figure 3: Unique focuses and interconnections of trustworthy frameworks