Fairness in AI Comparison Hub
6 papers - avg viability 4.8
Top Papers
- Fair Learning for Bias Mitigation and Quality Optimization in Paper Recommendation(7.0)
Fair-PaperRec optimizes paper acceptance decisions by mitigating demographic biases while enhancing quality.
- Intersectional Fairness via Mixed-Integer Optimization(5.0)
Develop interpretable AI models using Mixed-Integer Optimization to mitigate intersectional bias in regulated industries.
- SOMtime the World Ain$'$t Fair: Violating Fairness Using Self-Organizing Maps(5.0)
Develop a fairness auditing tool using Self-Organizing Maps for unsupervised machine learning models.
- Safe Fairness Guarantees Without Demographics in Classification: Spectral Uncertainty Set Perspective(4.0)
Develop a fairness-enhancing classifier that adjusts the spectrum of Fourier feature mapping without demographic data.
- Does Reasoning Make Search More Fair? Comparing Fairness in Reasoning and Non-Reasoning Rerankers(4.0)
This research evaluates the fairness of reasoning versus non-reasoning rerankers in information retrieval.
- Locating Demographic Bias at the Attention-Head Level in CLIP's Vision Encoder(4.0)
A mechanistic fairness audit tool that identifies demographic bias at the attention-head level in vision transformers.