A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes
This work addresses algorithmic bias in high-stakes decision-making for practitioners using Naive Bayes, but the method is incremental as it combines existing in-processing and post-processing techniques.
The authors propose a fairness-aware Naive Bayes classifier (BMNB) that uses a blended likelihood approach and adaptive thresholding to reduce bias. On three datasets, they achieve Disparate Impact values close to 1 and Equal Opportunity Difference values near 0, while maintaining computational efficiency.
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.