Bias originates from both data and algorithmic design, often exacerbated by traditional fairness methods that fail to address the subtle impacts of protected attributes. This study introduces an approach to mitigate bias in machine learning by leveraging model uncertainty. Our approach utilizes a multi-task learning (MTL) framework combined with Monte Carlo (MC) Dropout to assess and mitigate uncertainty in predictions related to protected labels. By incorporating MC Dropout, our framework quantifies prediction uncertainty, which is crucial in areas with vague decision boundaries, thereby enhancing model fairness. Our methodology integrates multi-objective learning through pareto-optimality to balance fairness and performance across various applications. We demonstrate the effectiveness and transferability of our approach across multiple datasets and enhance model explainability through saliency maps to interpret how input features influence predictions, thereby enhancing the interpretability of machine learning models in practical applications.

翻译：偏差源于数据和算法设计，而传统公平性方法往往未能处理受保护属性的细微影响，反而加剧了这一问题。本研究提出了一种利用模型不确定性来缓解机器学习中偏差的方法。我们的方法采用多任务学习框架，结合蒙特卡洛丢弃法，以评估和缓解与受保护标签相关的预测不确定性。通过引入蒙特卡洛丢弃法，我们的框架能够量化预测不确定性，这在决策边界模糊的领域中至关重要，从而提升了模型的公平性。我们的方法通过帕累托最优整合了多目标学习，以在各种应用中平衡公平性与性能。我们在多个数据集上验证了该方法的有效性和可迁移性，并通过显著性图增强了模型的可解释性，以阐明输入特征如何影响预测，从而提升了机器学习模型在实际应用中的可解释性。