Fair Decisions from Calibrated Scores: Achieving Optimal Classification While Satisfying Sufficiency
arXiv:2602.07285v2 Announce Type: replace Abstract: Binary classification based on predicted probabilities (scores) is a fundamental task in supervised machine learning. While thresholding scores is Bayes-optimal in the unconstrained setting, using a single threshold generally violates statistical group fairness constraints. Under independence (statistical parity) and separation (equalized odds), such thresholding suffices when the scores already satisfy the corresponding criterion. However, this does not extend to sufficiency: even perfectly group-calibrated scores -- including true class pro
The proliferation of AI systems in high-stakes decision-making necessitates robust frameworks for ensuring fairness, especially as ethical considerations become paramount in ML deployment.
Achieving optimal classification while satisfying sufficiency for statistical group fairness is a critical technical advancement for trustworthy AI, directly impacting the deployability and legal compliance of AI systems across various sectors.
This research outlines a method to achieve optimal classifier performance under fairness constraints, moving beyond prior limitations where fairness often compromised accuracy.
- · AI developers
- · Regulators
- · Industries deploying AI (e.g., finance, healthcare)
- · Individuals subject to AI decisions
- · Developers ignoring fairness principles
- · Systems with uncalibrated or unfair decision outputs
Improved fairness and reliability in AI-driven classification systems.
Increased public and regulatory trust in AI applications, accelerating adoption in sensitive domains.
The establishment of new industry best practices and potentially regulatory standards for fair AI deployments.
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Read at arXiv cs.LG