AI RESEARCH
A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes
arXiv CS.LG
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ArXi:2605.25228v1 Announce Type: new 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.