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[2602.22560] Operationalizing Fairness: Post-Hoc Threshold Optimization Under Hard Resource Limits
Summary
This paper presents a framework for optimizing decision thresholds in machine learning to balance fairness and resource constraints, ensuring compliance with ethical standards.
Why It Matters
As machine learning is increasingly deployed in critical areas, this research addresses the pressing need for fairness in algorithms while adhering to strict resource limits. The proposed framework offers a practical solution for stakeholders to navigate ethical trade-offs, making it relevant for industries facing regulatory scrutiny.
Key Takeaways
- Introduces a model-agnostic framework for threshold optimization under resource constraints.
- Demonstrates that capacity limits significantly influence ethical decision-making in machine learning.
- Proves that a single global decision threshold can ensure legal compliance while maintaining fairness.
- Shows that existing fairness heuristics may fail under strict resource limitations.
- Provides empirical evidence supporting the framework's effectiveness in high-stakes scenarios.
Computer Science > Machine Learning arXiv:2602.22560 (cs) [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 (experimental) 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 th...
