# Multi-dimensional discrimination in Law and Machine Learning -- A comparative overview

arXiv: https://arxiv.org/abs/2302.05995 · HTML: https://mlsanity.com/paper/2302.05995/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 99 references that are in the map.
- First posted on arXiv 12 Feb 2023.

## Builds on (most cited first, 26 of 26)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices](https://mlsanity.com/paper/1906.09208/) (2019)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [Towards a Critical Race Methodology in Algorithmic Fairness](https://mlsanity.com/paper/1912.03593/) (2019)
- [Fairness in Reinforcement Learning](https://mlsanity.com/paper/1611.03071/) (2016)
- [A Survey on Bias in Visual Datasets](https://mlsanity.com/paper/2107.07919/) (2021)
- [An Empirical Study of Rich Subgroup Fairness for Machine Learning](https://mlsanity.com/paper/1808.08166/) (2018)
- [Why Fairness Cannot Be Automated: Bridging the Gap Between EU Non-Discrimination Law and AI](https://mlsanity.com/paper/2005.05906/) (2020)
- [Subgroup Generalization and Fairness of Graph Neural Networks](https://mlsanity.com/paper/2106.15535/) (2021)
- [Fairness in Rankings and Recommendations: An Overview](https://mlsanity.com/paper/2104.05994/) (2021)
- [How Do Fair Decisions Fare in Long-term Qualification?](https://mlsanity.com/paper/2010.11300/) (2020)
- ["What We Can't Measure, We Can't Understand": Challenges to Demographic Data Procurement in the Pursuit of Fairness](https://mlsanity.com/paper/2011.02282/) (2020)
- [AdaFair: Cumulative Fairness Adaptive Boosting](https://mlsanity.com/paper/1909.08982/) (2019)
- [Characterizing Intersectional Group Fairness with Worst-Case Comparisons](https://mlsanity.com/paper/2101.01673/) (2021)
- [On Learning Fairness and Accuracy on Multiple Subgroups](https://mlsanity.com/paper/2210.10837/) (2022)
- [Bayesian Modeling of Intersectional Fairness: The Variance of Bias](https://mlsanity.com/paper/1811.07255/) (2018)
- [Fair Pipelines](https://mlsanity.com/paper/1707.00391/) (2017)
- [Causal intersectionality for fair ranking](https://mlsanity.com/paper/2006.08688/) (2020)
- [Fairness Implications of Encoding Protected Categorical Attributes](https://mlsanity.com/paper/2201.11358/) (2022)
- [Using sensitive data to prevent discrimination by artificial intelligence: Does the GDPR need a new exception?](https://mlsanity.com/paper/2206.03262/) (2022)
- [Multi-fairness under class-imbalance](https://mlsanity.com/paper/2104.13312/) (2021)
- [Legal perspective on possible fairness measures - A legal discussion using the example of hiring decisions (preprint)](https://mlsanity.com/paper/2108.06918/) (2021)

## Built on by (most cited first, 15 of 15)

- [Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://mlsanity.com/paper/2502.20897/) (2025)
- [Towards Standardizing AI Bias Exploration](https://mlsanity.com/paper/2405.19022/) (2024)
- [Intersectional Unfairness Discovery](https://mlsanity.com/paper/2405.20790/) (2024)
- [Towards Cohesion-Fairness Harmony: Contrastive Regularization in Individual Fair Graph Clustering](https://mlsanity.com/paper/2402.10756/) (2024)
- [FairBranch: Mitigating Bias Transfer in Fair Multi-task Learning](https://mlsanity.com/paper/2310.13746/) (2023)
- [Fair Enough? A map of the current limitations of the requirements to have fair algorithms](https://mlsanity.com/paper/2311.12435/) (2023)
- [Counterfactual Situation Testing: From Single to Multidimensional Discrimination](https://mlsanity.com/paper/2502.01267/) (2025)
- [Correcting Underrepresentation and Intersectional Bias for Classification](https://mlsanity.com/paper/2306.11112/) (2023)
- [Fairness Certification for Natural Language Processing and Large Language Models](https://mlsanity.com/paper/2401.01262/) (2024)
- [Intersectional Divergence: Measuring Fairness in Regression](https://mlsanity.com/paper/2505.00830/) (2025)
- [MMM-fair: An Interactive Toolkit for Exploring and Operationalizing Multi-Fairness Trade-offs](https://mlsanity.com/paper/2509.08156/) (2025)
- [JustEva: A Toolkit to Evaluate LLM Fairness in Legal Knowledge Inference](https://mlsanity.com/paper/2509.12104/) (2025)
- [Evaluating AI Group Fairness: a Fuzzy Logic Perspective](https://mlsanity.com/paper/2406.18939/) (2024)
- [It's complicated. The relationship of algorithmic fairness and non-discrimination provisions for high-risk systems in the EU AI Act](https://mlsanity.com/paper/2501.12962/) (2025)
- [A Deep Latent Factor Graph Clustering with Fairness-Utility Trade-off Perspective](https://mlsanity.com/paper/2510.23507/) (2025)

## Abstract (arXiv)

AI-driven decision-making can lead to discrimination against certain individuals or social groups based on protected characteristics/attributes such as race, gender, or age. The domain of fairness-aware machine learning focuses on methods and algorithms for understanding, mitigating, and accounting for bias in AI/ML models. Still, thus far, the vast majority of the proposed methods assess fairness based on a single protected attribute, e.g. only gender or race. In reality, though, human identities are multi-dimensional, and discrimination can occur based on more than one protected characteristic, leading to the so-called ``multi-dimensional discrimination'' or ``multi-dimensional fairness'' problem. While well-elaborated in legal literature, the multi-dimensionality of discrimination is less explored in the machine learning community. Recent approaches in this direction mainly follow the so-called intersectional fairness definition from the legal domain, whereas other notions like additive and sequential discrimination are less studied or not considered thus far. In this work, we overview the different definitions of multi-dimensional discrimination/fairness in the legal domain as well as how they have been transferred/ operationalized (if) in the fairness-aware machine learning domain. By juxtaposing these two domains, we draw the connections, identify the limitations, and point out open research directions.