# A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set

arXiv: https://arxiv.org/abs/2003.14263 · HTML: https://mlsanity.com/paper/2003.14263/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 30 of 35)

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- [Tackling Bias in Pre-trained Language Models: Current Trends and Under-represented Societies](https://mlsanity.com/paper/2312.01509/) (2023)
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- [Representative & Fair Synthetic Data](https://mlsanity.com/paper/2104.03007/) (2021)
- [How Biased are Your Features?: Computing Fairness Influence Functions with Global Sensitivity Analysis](https://mlsanity.com/paper/2206.00667/) (2022)
- [End-To-End Bias Mitigation: Removing Gender Bias in Deep Learning](https://mlsanity.com/paper/2104.02532/) (2021)
- [The World According to LLMs: How Geographic Origin Influences LLMs' Entity Deduction Capabilities](https://mlsanity.com/paper/2508.05525/) (2025)
- [Measuring What Matters: Connecting AI Ethics Evaluations to System Attributes, Hazards, and Harms](https://mlsanity.com/paper/2510.10339/) (2025)
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- [Exposing the Illusion of Fairness: Auditing Vulnerabilities to Distributional Manipulation Attacks](https://mlsanity.com/paper/2507.20708/) (2025)
- [A Novel Regularization Approach to Fair ML](https://mlsanity.com/paper/2208.06557/) (2022)
- [Are fairness metric scores enough to assess discrimination biases in machine learning?](https://mlsanity.com/paper/2306.05307/) (2023)
- [Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation](https://mlsanity.com/paper/2509.08163/) (2025)
- [How optimal transport can tackle gender biases in multi-class neural-network classifiers for job recommendations?](https://mlsanity.com/paper/2302.14063/) (2023)
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- [P2NIA: Privacy-Preserving Non-Iterative Auditing](https://mlsanity.com/paper/2504.00874/) (2025)
- [To what extent should we trust AI models when they extrapolate?](https://mlsanity.com/paper/2201.11260/) (2022)
- [Uncertainty in Fairness Assessment: Maintaining Stable Conclusions Despite Fluctuations](https://mlsanity.com/paper/2302.01079/) (2023)
- [When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness](https://mlsanity.com/paper/2302.07185/) (2023)
- [Debiasing Machine Learning Models by Using Weakly Supervised Learning](https://mlsanity.com/paper/2402.15477/) (2024)
- [Fair Graph Neural Network with Supervised Contrastive Regularization](https://mlsanity.com/paper/2404.06090/) (2024)
- [When majority rules, minority loses: bias amplification of gradient descent](https://mlsanity.com/paper/2505.13122/) (2025)
- [Size-adaptive Hypothesis Testing for Fairness](https://mlsanity.com/paper/2506.10586/) (2025)
- [Fairness constraint in Structural Econometrics and Application to fair estimation using Instrumental Variables](https://mlsanity.com/paper/2202.08977/) (2022)
- [Cluster-level Group Representativity Fairness in k-means Clustering](https://mlsanity.com/paper/2212.14467/) (2022)
- [Dancing in the Shadows: Harnessing Ambiguity for Fairer Classifiers](https://mlsanity.com/paper/2406.19066/) (2024)

## Abstract (arXiv)

Applications based on Machine Learning models have now become an indispensable part of the everyday life and the professional world. A critical question then recently arised among the population: Do algorithmic decisions convey any type of discrimination against specific groups of population or minorities? In this paper, we show the importance of understanding how a bias can be introduced into automatic decisions. We first present a mathematical framework for the fair learning problem, specifically in the binary classification setting. We then propose to quantify the presence of bias by using the standard Disparate Impact index on the real and well-known Adult income data set. Finally, we check the performance of different approaches aiming to reduce the bias in binary classification outcomes. Importantly, we show that some intuitive methods are ineffective. This sheds light on the fact trying to make fair machine learning models may be a particularly challenging task, in particular when the training observations contain a bias.