#algorithmic-fairness2 articles

2 articles

AINeutralarXiv – CS AI · Feb 277/107

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Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits

Researchers developed a new framework for deploying AI systems in high-stakes environments that balances safety, fairness, and efficiency under strict resource constraints. The study found that capacity limits dominate ethical considerations, determining deployment thresholds in over 80% of tested scenarios while maintaining better performance than traditional fairness approaches.

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AINeutralarXiv – CS AI · Feb 275/106

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From Bias to Balance: Fairness-Aware Paper Recommendation for Equitable Peer Review

Researchers developed Fair-PaperRec, an AI system that uses fairness regularization to reduce bias in academic peer review processes. The system achieved up to 42% increased participation from underrepresented groups while maintaining scholarly quality with minimal utility loss.

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