Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specific bias metrics. Existing methods still struggle with intersectionality, multiclass settings, and limited flexibility and generality. To address these gaps, we introduce MIFair, a unified framework for bias assessment and mitigation based on mutual information. MIFair provides a flexible metric template and an in-processing mitigation method inspired by the Prejudice Remover, defining group fairness as statistical independence between prediction-derived variables and sensitive attributes. We further strengthen its information-theoretic foundation by establishing equivalences with widely used fairness notions such as independence and separation. MIFair naturally supports intersectionality, complex subgroup structures, and multiclass classification and employs regularization-based training to reduce bias according to the selected metric. Its key advantage is its versatility: it consolidates diverse fairness requirements into a single coherent framework, enabling consistent benchmarking and simplifying practical use. Experiments on real-world tabular and image datasets show that MIFair effectively reduces bias, including previously unaddressed multi-attribute scenarios, while maintaining strong predictive performance across the evaluated settings.

翻译：机器学习中的公平性因其伦理复杂性、缺乏统一定义以及需要特定于上下文的偏见度量而仍然具有挑战性。现有方法在处理交叉性、多类别场景以及有限灵活性和泛化性方面仍存在困难。为弥补这些不足，我们提出MIFair，一个基于互信息的偏见评估与缓解统一框架。MIFair提供了一个灵活的度量模板，以及一种受偏见消除器启发的处理中缓解方法，将群体公平性定义为预测衍生变量与敏感属性之间的统计独立性。我们进一步通过与独立性、分离性等广泛使用的公平性概念建立等价关系，强化其信息理论基础。MIFair天然支持交叉性、复杂的子群体结构以及多类别分类，并采用基于正则化的训练根据所选度量减少偏见。其关键优势在于通用性：它将多样的公平性需求整合到一个连贯的框架中，从而支持一致的基准测试并简化实际应用。在现实表格与图像数据集上的实验表明，MIFair能有效减少偏见（包括之前未涉及的多属性场景），同时在所评估的设置中保持强预测性能。