# Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features

arXiv: https://arxiv.org/abs/2202.11499 · HTML: https://mlsanity.com/paper/2202.11499/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 20 of 20)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints](https://mlsanity.com/paper/1707.09457/) (2017)
- [Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness](https://mlsanity.com/paper/1711.05144/) (2017)
- [Empirical Risk Minimization under Fairness Constraints](https://mlsanity.com/paper/1802.08626/) (2018)
- [A comparative study of fairness-enhancing interventions in machine learning](https://mlsanity.com/paper/1802.04422/) (2018)
- [An Intersectional Definition of Fairness](https://mlsanity.com/paper/1807.08362/) (2018)
- [A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity](https://mlsanity.com/paper/1809.03400/) (2018)
- [Downstream Effects of Affirmative Action](https://mlsanity.com/paper/1808.09004/) (2018)
- [On the Moral Justification of Statistical Parity](https://mlsanity.com/paper/2011.02079/) (2020)
- [Auditing and Achieving Intersectional Fairness in Classification Problems](https://mlsanity.com/paper/1911.01468/) (2019)
- [General Fair Empirical Risk Minimization](https://mlsanity.com/paper/1901.10080/) (2019)
- [Group Fairness: Independence Revisited](https://mlsanity.com/paper/2101.02968/) (2021)
- [Causal Multi-Level Fairness](https://mlsanity.com/paper/2010.07343/) (2020)
- [Beyond Individual and Group Fairness](https://mlsanity.com/paper/2008.09490/) (2020)
- [On preserving non-discrimination when combining expert advice](https://mlsanity.com/paper/1810.11829/) (2018)
- [On conditional parity as a notion of non-discrimination in machine learning](https://mlsanity.com/paper/1706.08519/) (2017)

## Built on by (most cited first, 3 of 3)

- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [On the Impact of Data Quality on Image Classification Fairness](https://mlsanity.com/paper/2305.01595/) (2023)
- [A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes](https://mlsanity.com/paper/2605.25228/) (2026)

## Abstract (arXiv)

Fairness-aware machine learning seeks to maximise utility in generating predictions while avoiding unfair discrimination based on sensitive attributes such as race, sex, religion, etc. An important line of work in this field is enforcing fairness during the training step of a classifier. A simple yet effective binary classification algorithm that follows this strategy is two-naive-Bayes (2NB), which enforces statistical parity - requiring that the groups comprising the dataset receive positive labels with the same likelihood. In this paper, we generalise this algorithm into N-naive-Bayes (NNB) to eliminate the simplification of assuming only two sensitive groups in the data and instead apply it to an arbitrary number of groups. We propose an extension of the original algorithm's statistical parity constraint and the post-processing routine that enforces statistical independence of the label and the single sensitive attribute. Then, we investigate its application on data with multiple sensitive features and propose a new constraint and post-processing routine to enforce differential fairness, an extension of established group-fairness constraints focused on intersectionalities. We empirically demonstrate the effectiveness of the NNB algorithm on US Census datasets and compare its accuracy and debiasing performance, as measured by disparate impact and DF-ε score, with similar group-fairness algorithms. Finally, we lay out important considerations users should be aware of before incorporating this algorithm into their application, and direct them to further reading on the pros, cons, and ethical implications of using statistical parity as a fairness criterion.