Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits
#Machine Learning #Algorithmic Fairness #Resource Constraints #Post-hoc Optimization #Anti-discrimination #High-stakes Domains #Decision Thresholds #Ethical Trade-offs
📌 Key Takeaways
- Researchers developed a framework balancing ML safety and fairness under strict resource constraints
- Existing fairness interventions often violate anti-discrimination laws with group-specific thresholds
- Capacity constraints determine outcomes in over 80% of tested configurations
- The framework maintains high risk identification even under restrictive 25% capacity limits
- Fairness objectives must be subordinated to operational capacity limits for viable deployment
📖 Full Retelling
Researchers Moirangthem Tiken Singh, Amit Kalita, and Sapam Jitu Singh introduced a novel post-hoc threshold optimization framework for machine learning systems on February 26, 2026, through their publication on arXiv. Their work addresses the critical challenge of balancing predictive safety and algorithmic fairness in high-stakes domains while operating under strict resource constraints, a pressing concern as machine learning systems increasingly influence decisions in areas like healthcare, finance, and criminal justice. The researchers identified significant limitations in existing fairness interventions that often assume unconstrained resources and employ group-specific decision thresholds that violate anti-discrimination regulations. Their proposed framework mathematically prevents intervention volumes from exceeding available resources by enforcing a single, global decision threshold to ensure legal compliance. Through extensive experimental evaluations on diverse high-stakes datasets, the researchers demonstrated that capacity constraints overwhelmingly dominate ethical priorities, with strict resource limits determining the final deployed threshold in over 80% of tested configurations. Notably, under a restrictive 25% capacity limit, their framework maintained high risk identification (recall ranging from 0.409 to 0.702), while standard unconstrained fairness heuristics collapsed to near-zero utility.
🏷️ Themes
Machine Learning Ethics, Resource Allocation, Algorithmic Fairness
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Original Source
--> Computer Science > Machine Learning arXiv:2602.22560 [Submitted on 26 Feb 2026] Title: Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits Authors: Moirangthem Tiken Singh , Amit Kalita , Sapam Jitu Singh View a PDF of the paper titled Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits, by Moirangthem Tiken Singh and 2 other authors View PDF HTML Abstract: The deployment of machine learning in high-stakes domains requires a balance between predictive safety and algorithmic fairness. However, existing fairness interventions often as- sume unconstrained resources and employ group-specific decision thresholds that violate anti- discrimination regulations. We introduce a post-hoc, model-agnostic threshold optimization framework that jointly balances safety, efficiency, and equity under strict and hard capacity constraints. To ensure legal compliance, the framework enforces a single, global decision thresh- old. We formulated a parameterized ethical loss function coupled with a bounded decision rule that mathematically prevents intervention volumes from exceeding the available resources. An- alytically, we prove the key properties of the deployed threshold, including local monotonicity with respect to ethical weighting and the formal identification of critical capacity regimes. We conducted extensive experimental evaluations on diverse high-stakes datasets. The principal re- sults demonstrate that capacity constraints dominate ethical priorities; the strict resource limit determines the final deployed threshold in over 80% of the tested configurations. Furthermore, under a restrictive 25% capacity limit, the proposed framework successfully maintains high risk identification (recall ranging from 0.409 to 0.702), whereas standard unconstrained fairness heuristics collapse to a near-zero utility. We conclude that theoretical fairness objectives must be explicitly subordinated to operational capacity li...
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