We propose a unique solution to tackle the often-competing goals of fairness and utility in machine learning classification tasks. While fairness ensures that the model's predictions are unbiased and do not discriminate against any particular group, utility focuses on maximizing the accuracy of the model's predictions. Our aim is to investigate the relationship between uncertainty and fairness. Our approach leverages this concept by employing Bayesian learning to estimate the uncertainty in sample predictions where the estimation is independent of confounding effects related to the protected attribute. Through empirical evidence, we show that samples with low classification uncertainty are modeled more accurately and fairly than those with high uncertainty, which may have biased representations and higher prediction errors. To address the challenge of balancing fairness and utility, we propose a novel fairness-utility objective that is defined based on uncertainty quantification. The weights in this objective are determined by the level of uncertainty, allowing us to optimize both fairness and utility simultaneously. Experiments on real-world datasets demonstrate the effectiveness of our approach. Our results show that our method outperforms state-of-the-art methods in terms of the fairness-utility tradeoff and this applies to both group and individual fairness metrics. This work presents a fresh perspective on the trade-off between accuracy and fairness in machine learning and highlights the potential of using uncertainty as a means to achieve optimal fairness and utility.

翻译：我们提出了一种独特的解决方案，以应对机器学习分类任务中公平性与效用性这两个通常相互冲突的目标。公平性确保模型的预测无偏，不歧视任何特定群体，而效用性则侧重于最大化模型预测的准确性。我们的目标是探究不确定性与公平性之间的关系。该方法利用贝叶斯学习来估计样本预测中的不确定性，且该估计独立于与受保护属性相关的混杂效应。通过实验证据表明，分类不确定性较低的样本比高不确定性样本能得到更准确、更公平的建模，后者可能存在有偏表示和更高的预测误差。为平衡公平性与效用性的挑战，我们提出了一种基于不确定性量化的新型公平-效用目标函数。该目标中的权重由不确定性水平决定，从而能够同时优化公平性与效用性。在真实数据集上的实验证明了我们方法的有效性。结果表明，我们的方法在公平-效用权衡方面优于现有先进方法，且这一优势同时适用于群组公平性和个体公平性指标。本研究为机器学习中准确性与公平性之间的权衡提供了新视角，并凸显了利用不确定性实现最优公平性与效用性的潜力。