mlsanityData to 14 Oct 2026

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

arXiv 2412.15114 ↗First posted 19 Dec 2024cs.AI cs.CYcited by 2

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 21 of its 117 references that are in the map. First posted on arXiv 19 Dec 2024.

Builds on

21 of 117 references.

References · 21

  1. 2016Concrete Problems in AI Safetycited by 1,642
  2. 2023Large Language Model Alignment: A Surveycited by 243
  3. 2022From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagationcited by 141
  4. 2014Differential Privacy and Machine Learning: a Survey and Reviewcited by 67
  5. 2022A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advancescited by 59
  6. 2021Privacy-Preserving Machine Learning: Methods, Challenges and Directionscited by 58
  7. 2020Improving the Fairness of Deep Generative Models without Retrainingcited by 46
  8. 2024How Far Are We From AGI: Are LLMs All We Need?cited by 31
  9. 2018Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function)cited by 22
  10. 2024Explainable Artificial Intelligence for Medical Applications: A Reviewcited by 12
  11. 2024Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyondcited by 10
  12. 2024GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryptioncited by 7
9 more
  1. 2024Affective Computing Has Changed: The Foundation Model Disruptioncited by 7
  2. 2021Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluationscited by 4
  3. 2024The Case for Animal-Friendly AIcited by 4
  4. 2024Federated Learning with Differential Privacycited by 3
  5. 2024Enrolment-based personalisation for improving individual-level fairness in speech emotion recognitioncited by 3
  6. 2024Audio-based Kinship Verification Using Age Domain Conversioncited by 3
  7. 2014Friendly Artificial Intelligence: the Physics Challengecited by 2
  8. 2023Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metricscited by 1
  9. 2024Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learningcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studiescited by 1
  2. 2026AI Alignment through a Game-theoretic Lens: A Surveycited by 0