Fair Enough? A map of the current limitations of the requirements to have fair algorithms
Not on the map. Why?
Abstract (arXiv)
In recent years, the increase in the usage and efficiency of Artificial Intelligence and, more in general, of Automated Decision-Making systems has brought with it an increasing and welcome awareness of the risks associated with such systems. One of such risks is that of perpetuating or even amplifying bias and unjust disparities present in the data from which many of these systems learn to adjust and optimise their decisions. This awareness has on the one hand encouraged several scientific communities to come up with more and more appropriate ways and methods to assess, quantify, and possibly mitigate such biases and disparities. On the other hand, it has prompted more and more layers of society, including policy makers, to call for fair algorithms. We believe that while many excellent and multidisciplinary research is currently being conducted, what is still fundamentally missing is the awareness that having fair algorithms is per se a nearly meaningless requirement that needs to be complemented with many additional social choices to become actionable. Namely, there is a hiatus between what the society is demanding from Automated Decision-Making systems, and what this demand actually means in real-world scenarios. In this work, we outline the key features of such a hiatus and pinpoint a set of crucial open points that we as a society must address in order to give a concrete meaning to the increasing demand of fairness in Automated Decision-Making systems.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 98 references that are in the map. First posted on arXiv 21 Nov 2023.
Builds on
22 of 98 references.
References · 22
- 2017Counterfactual Fairnesscited by 802
- 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 582
- 2017Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairnesscited by 381
- 2018AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Biascited by 319
- 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
- 2018Path-Specific Counterfactual Fairnesscited by 179
- 2018Troubling Trends in Machine Learning Scholarshipcited by 107
- 201850 Years of Test (Un)fairness: Lessons for Machine Learningcited by 101
- 2021A Clarification of the Nuances in the Fairness Metrics Landscapecited by 98
- 2019Fairwashing: the risk of rationalizationcited by 81
- 2020Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AIcited by 81
- 2023Fairlearn: Assessing and Improving Fairness of AI Systemscited by 74
10 more
- 2022Causal Fairness Analysiscited by 42
- 2021Emergent Unfairness in Algorithmic Fairness-Accuracy Trade-Off Researchcited by 34
- 2021Fairness Perceptions of Algorithmic Decision-Making: A Systematic Review of the Empirical Literaturecited by 27
- 2021A Sociotechnical View of Algorithmic Fairnesscited by 24
- 2020Machine learning fairness notions: Bridging the gap with real-world applicationscited by 19
- 2023The Possibility of Fairness: Revisiting the Impossibility Theorem in Practicecited by 18
- 2023Multi-dimensional discrimination in Law and Machine Learning -- A comparative overviewcited by 15
- 2020Dimensions of Diversity in Human Perceptions of Algorithmic Fairnesscited by 13
- 2022Inherent Limitations of AI Fairnesscited by 12
- 2022Subverting machines, fluctuating identities: Re-learning human categorizationcited by 10
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Citing papers per year: 2025: 2.
2 papers.