Fairness through Aleatoric Uncertainty
Fairness through Aleatoric Uncertainty
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DOI：
10.1145/3583780.3614875
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
Anique Tahir;Lu Cheng;Huan Liu
中科院分区：
文献类型：
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作者：
Anique Tahir;Lu Cheng;Huan Liu
我们提出了一个简单而有效的解决方案，以解决分类任务中经常相互竞争的公平性和实用性目标。虽然公平性确保模型的预测是无偏的，不歧视任何特定的群体或个人，但效用侧重于最大限度地提高模型的预测性能。这项工作引入了利用任意不确定性的想法（例如，数据模糊性）以改善公平性-效用权衡。我们的中心假设是任意的不确定性是算法公平性的关键因素，具有低任意不确定性的样本比具有高任意不确定性的样本更准确和公平地建模。然后，我们提出了一个原则性的模型，以提高公平性时，任意的不确定性很高，并提高其他地方的效用。我们的方法首先干预数据分布，以更好地解耦任意的不确定性和认知的不确定性。然后，它引入了一个公平-效用双目标损失的基础上估计的任意不确定性定义。我们的方法从理论上保证，以提高公平性效用权衡。在表格和图像数据集上的实验结果表明，该方法的性能优于最先进的方法w.r.t.公平-效用权衡和w.r.t.组和个体公平性度量。这项工作为效用和算法公平性之间的权衡提供了一个新的视角，并为在公平机器学习中使用预测不确定性的潜力开辟了一条关键途径。
We propose a simple yet effective solution to tackle the often-competing goals of fairness and utility in classification tasks. While fairness ensures that the model's predictions are unbiased and do not discriminate against any particular group or individual, utility focuses on maximizing the model's predictive performance. This work introduces the idea of leveraging aleatoric uncertainty (e.g., data ambiguity) to improve the fairness-utility trade-off. Our central hypothesis is that aleatoric uncertainty is a key factor for algorithmic fairness and samples with low aleatoric uncertainty are modeled more accurately and fairly than those with high aleatoric uncertainty. We then propose a principled model to improve fairness when aleatoric uncertainty is high and improve utility elsewhere. Our approach first intervenes in the data distribution to better decouple aleatoric uncertainty and epistemic uncertainty. It then introduces a fairness-utility bi-objective loss defined based on the estimated aleatoric uncertainty. Our approach is theoretically guaranteed to improve the fairness-utility trade-off. Experimental results on both tabular and image datasets show that the proposed approach outperforms state-of-the-art methods w.r.t. the fairness-utility trade-off and w.r.t. both group and individual fairness metrics. This work presents a fresh perspective on the trade-off between utility and algorithmic fairness and opens a key avenue for the potential of using prediction uncertainty in fair machine learning.