Research map / Learning theory and algorithms / Fairness
Learning Fair Naive Bayes Classifiers by Discovering and Eliminating Discrimination Patterns
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add δ-fairness constraints during learning | Naive Bayes · COMPAS | log-likelihood: -207,055 -> -207,395 | Hurt |
| Add δ-fairness constraints during learning | Naive Bayes · Adult | log-likelihood: -226,375 -> -228,763 | Hurt |
| Add δ-fairness constraints during learning | Naive Bayes · German | log-likelihood: -12,630 -> -12,635 | Hurt |
| Enforce sensitive-variable independence from the decision | Naive Bayes · COMPAS | log-likelihood: -207,055 -> -208,639 | Hurt |
| Enforce sensitive-variable independence from the decision | Naive Bayes · Adult | log-likelihood: -226,375 -> -232,180 | Hurt |
| Enforce sensitive-variable independence from the decision | Naive Bayes · German | log-likelihood: -12,630 -> -12,649 | Hurt |
| Add δ-fairness constraints during learning | Naive Bayes · COMPAS | accuracy: 0.880 -> 0.879 | Hurt |
| Add δ-fairness constraints during learning | Naive Bayes · Adult | accuracy: 0.811 -> 0.827 | Helped |
| Add δ-fairness constraints during learning | Naive Bayes · German | accuracy: 0.690 -> 0.696 | Helped |
| Use δ-fair learning instead of the two-naive-Bayes method | Fair Naive Bayes classification · COMPAS | accuracy: 0.875 -> 0.879 | Helped |
| Use δ-fair learning instead of the two-naive-Bayes method | Fair Naive Bayes classification · Adult | accuracy: 0.759 -> 0.827 | Helped |
| Use δ-fair learning instead of the two-naive-Bayes method | Fair Naive Bayes classification · German | accuracy: 0.679 -> 0.696 | Helped |
Closest papers by meaning
- Certifying Fairness of Probabilistic Circuits (AAAI 2023 · similarity 0.91)
- Bayes-Optimal Fair Classification with Multiple Sensitive Features (AAAI 2026 · similarity 0.89)
- A Reductions Approach to Fair Classification (ICML 2018 · similarity 0.88)
- Fairness-aware Bayes Optimal Functional Classification (NeurIPS 2025 · similarity 0.86)
- Post-hoc bias scoring is optimal for fair classification (ICLR 2024 · similarity 0.86)
- Individual Fairness In Strategic Classification (NeurIPS 2025 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.