arxiv
PublishedMay 26, 2026 at 4:00 AM
A Blended Likelihood Approach for Achieving Fairness Using Naive Bayes
Publisher summary· verbatim
arXiv:2605.25228v1 Announce Type: new Abstract: 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 an
Stay posted· Newsletter
Three short emails a week: Monday, Wednesday and Friday. Top movers, new models and the most-referenced story.
Your email is used only to send The Bubble Brief. Privacy policy
Discussion
No replies yet. Be first.
Related coverage
More from ARXIV
arxivBridging the Gap: Converting Read Text to Conversational Dialogue1harxivTruth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance1harxivRICE-Alpha: Reliability-Informed Correction with Event Graphs for LLM-Agent Stock Forecasting1harxivWitness Overlap: Directional Provenance Inside Open-Weight Model Families1hThe Bubble Brief
MON · WED · FRIRead AI insights every Monday, Wednesday and Friday — top movers, new models, story of the week.
Your email is used only to send The Bubble Brief. Privacy policy
Originally published on arxiv ↗