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

arXiv: https://arxiv.org/abs/2412.15114 · HTML: https://mlsanity.com/paper/2412.15114/

## 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 (most cited first, 21 of 21)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Large Language Model Alignment: A Survey](https://mlsanity.com/paper/2309.15025/) (2023)
- [From Attribution Maps to Human-Understandable Explanations through Concept Relevance Propagation](https://mlsanity.com/paper/2206.03208/) (2022)
- [Differential Privacy and Machine Learning: a Survey and Review](https://mlsanity.com/paper/1412.7584/) (2014)
- [A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances](https://mlsanity.com/paper/2203.06935/) (2022)
- [Privacy-Preserving Machine Learning: Methods, Challenges and Directions](https://mlsanity.com/paper/2108.04417/) (2021)
- [Improving the Fairness of Deep Generative Models without Retraining](https://mlsanity.com/paper/2012.04842/) (2020)
- [How Far Are We From AGI: Are LLMs All We Need?](https://mlsanity.com/paper/2405.10313/) (2024)
- [Impossibility and Uncertainty Theorems in AI Value Alignment (or why your AGI should not have a utility function)](https://mlsanity.com/paper/1901.00064/) (2018)
- [Explainable Artificial Intelligence for Medical Applications: A Review](https://mlsanity.com/paper/2412.01829/) (2024)
- [Affective Computing for Healthcare: Recent Trends, Applications, Challenges, and Beyond](https://mlsanity.com/paper/2402.13589/) (2024)
- [GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2401.14840/) (2024)
- [Affective Computing Has Changed: The Foundation Model Disruption](https://mlsanity.com/paper/2409.08907/) (2024)
- [Fairness and underspecification in acoustic scene classification: The case for disaggregated evaluations](https://mlsanity.com/paper/2110.01506/) (2021)
- [The Case for Animal-Friendly AI](https://mlsanity.com/paper/2403.01199/) (2024)
- [Federated Learning with Differential Privacy](https://mlsanity.com/paper/2402.02230/) (2024)
- [Enrolment-based personalisation for improving individual-level fairness in speech emotion recognition](https://mlsanity.com/paper/2406.06665/) (2024)
- [Audio-based Kinship Verification Using Age Domain Conversion](https://mlsanity.com/paper/2410.11120/) (2024)
- [Friendly Artificial Intelligence: the Physics Challenge](https://mlsanity.com/paper/1409.0813/) (2014)
- [Kantian Deontology Meets AI Alignment: Towards Morally Grounded Fairness Metrics](https://mlsanity.com/paper/2311.05227/) (2023)
- [Trading through Earnings Seasons using Self-Supervised Contrastive Representation Learning](https://mlsanity.com/paper/2409.17392/) (2024)

## Built on by (most cited first, 2 of 2)

- [Imperfectly Cooperative Human-AI Interactions: Comparing the Impacts of Human and AI Attributes in Simulated and User Studies](https://mlsanity.com/paper/2604.15607/) (2026)
- [AI Alignment through a Game-theoretic Lens: A Survey](https://mlsanity.com/paper/2608.27910/) (2026)

## 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.