# Inherent Limitations of AI Fairness

arXiv: https://arxiv.org/abs/2212.06495 · HTML: https://mlsanity.com/paper/2212.06495/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2022.

## Built on by (most cited first, 12 of 12)

- [Analyzing Quality, Bias, and Performance in Text-to-Image Generative Models](https://mlsanity.com/paper/2407.00138/) (2024)
- [fairret: a Framework for Differentiable Fairness Regularization Terms](https://mlsanity.com/paper/2310.17256/) (2023)
- [ABCFair: an Adaptable Benchmark approach for Comparing Fairness Methods](https://mlsanity.com/paper/2409.16965/) (2024)
- [Fairness Hacking: The Malicious Practice of Shrouding Unfairness in Algorithms](https://mlsanity.com/paper/2311.06826/) (2023)
- [Fair Enough? A map of the current limitations of the requirements to have fair algorithms](https://mlsanity.com/paper/2311.12435/) (2023)
- [Fairness Auditing with Multi-Agent Collaboration](https://mlsanity.com/paper/2402.08522/) (2024)
- [Runtime Monitoring and Enforcement of Conditional Fairness in Generative AIs](https://mlsanity.com/paper/2404.16663/) (2024)
- [Rolling in the deep of cognitive and AI biases](https://mlsanity.com/paper/2407.21202/) (2024)
- [A Multimodal GUI Architecture for Interfacing with LLM-Based Conversational Assistants](https://mlsanity.com/paper/2510.06223/) (2025)
- [Computational Compliance for AI Regulation: Blueprint for a New Research Domain](https://mlsanity.com/paper/2601.04474/) (2026)
- [Measuring Individual User Fairness with User Similarity and Effectiveness Disparity](https://mlsanity.com/paper/2602.02516/) (2026)
- [No-Free-Fairness: Fundamental Limits and Trade-offs in Learning Systems](https://mlsanity.com/paper/2606.17810/) (2026)

## Abstract (arXiv)

As the real-world impact of Artificial Intelligence (AI) systems has been steadily growing, so too have these systems come under increasing scrutiny. In response, the study of AI fairness has rapidly developed into a rich field of research with links to computer science, social science, law, and philosophy. Many technical solutions for measuring and achieving AI fairness have been proposed, yet their approach has been criticized in recent years for being misleading, unrealistic and harmful. In our paper, we survey these criticisms of AI fairness and identify key limitations that are inherent to the prototypical paradigm of AI fairness. By carefully outlining the extent to which technical solutions can realistically help in achieving AI fairness, we aim to provide the background necessary to form a nuanced opinion on developments in fair AI. This delineation also provides research opportunities for non-AI solutions peripheral to AI systems in supporting fair decision processes.