# A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes

arXiv: https://arxiv.org/abs/2605.25228 · HTML: https://mlsanity.com/paper/2605.25228/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 22 references that are in the map.
- First posted on arXiv 24 May 2026.

## Builds on (most cited first, 14 of 14)

- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [On Fairness and Calibration](https://mlsanity.com/paper/1709.02012/) (2017)
- [The Measure and Mismeasure of Fairness](https://mlsanity.com/paper/1808.00023/) (2018)
- [Fairness And Bias in Artificial Intelligence: A Brief Survey of Sources, Impacts, And Mitigation Strategies](https://mlsanity.com/paper/2304.07683/) (2023)
- [Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification](https://mlsanity.com/paper/1906.05082/) (2019)
- [Bias and Discrimination in AI: a cross-disciplinary perspective](https://mlsanity.com/paper/2008.07309/) (2020)
- [Group-Aware Threshold Adaptation for Fair Classification](https://mlsanity.com/paper/2111.04271/) (2021)
- [Removing biased data to improve fairness and accuracy](https://mlsanity.com/paper/2102.03054/) (2021)
- [Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns](https://mlsanity.com/paper/1906.03843/) (2019)
- [Fairness Score and Process Standardization: Framework for Fairness Certification in Artificial Intelligence Systems](https://mlsanity.com/paper/2201.06952/) (2022)
- [Fairness in machine learning: against false positive rate equality as a measure of fairness](https://mlsanity.com/paper/2007.02890/) (2020)
- [Algorithmic Bias in Recidivism Prediction: A Causal Perspective](https://mlsanity.com/paper/1911.10640/) (2019)
- [Fairness-Aware Naive Bayes Classifier for Data with Multiple Sensitive Features](https://mlsanity.com/paper/2202.11499/) (2022)
- [A Review of Fairness and A Practical Guide to Selecting Context-Appropriate Fairness Metrics in Machine Learning](https://mlsanity.com/paper/2411.06624/) (2024)

## Abstract (arXiv)

Concerns about algorithmic bias and fairness have increased as artificial intelligence has been incorporated into high-stakes decision-making. Traditional Naive Bayes classifiers, while efficient and interpretable, lack fairness-awareness mechanisms and perpetuate historical biases in sensitive domains such as hiring, credit scoring, and criminal justice. This study develops a fairness-aware extension of the Naive Bayes classifier that mitigates bias while maintaining computational efficiency. We propose the Bias Mitigating Naive Bayes (BMNB) classifier, integrating in-processing and post-processing interventions. The in-processing stage employs a blended likelihood approach combining group-specific and pooled likelihood estimates through a tunable blending parameter alpha to balance fairness and accuracy. The post-processing stage applies output calibration with adaptive thresholding to fine-tune group-specific decision boundaries. Experimental results indicate that BMNB attains Disparate Impact (DI) values of 1.000, 1.171, and 0.997 and Equal Opportunity Difference (EOD) values of -0.217, -0.226, and -0.053 on the Adult, ProPublica, and Framingham datasets, respectively, while maintaining computational efficiency. Ablation studies confirm that the combination of blended likelihood and adaptive thresholding yields superior performance compared to either technique in isolation.