Demystifying the Optimal Fair Classifier in Multi-Class Classification

Ensuring fair and equitable treatment across diverse groups, particularly in multi-class classification tasks, poses a significant challenge due to the persistent biases inherent in machine learning models. Most existing bias mitigation techniques are tailored to binary settings, and the presence of multi-dimensional outputs and complex fairness mechanisms makes their extension to multi-class scenarios neither straightforward nor effective. In this paper, we investigate two fundamental, unresolv

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ethics.ai (30 May 2026), “Demystifying the Optimal Fair Classifier in Multi-Class Classification,” evidence record 3392, https://ethics.ai/record/3392 (originally published by arXiv).

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