Fix Fairness, Don’t Ruin Accuracy: Performance Aware Fairness Repair using AutoML
Fix Fairness, Don’t Ruin Accuracy: Performance Aware Fairness Repair using AutoML
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DOI：
10.1145/3611643.3616257
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发表时间：
2023-06
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通讯作者：
Giang Nguyen-;Sumon Biswas;Hridesh Rajan
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作者：
Giang Nguyen-;Sumon Biswas;Hridesh Rajan
机器学习（ML）越来越多地用于关键决策软件中，但事件提出了有关ML预测公平性的问题。为了解决这个问题，需要新的工具和方法来减轻基于ML的软件的偏见。先前的研究提出了仅在特定情况下起作用的偏置缓解算法，并且通常会导致准确性丧失。我们提出的解决方案是一种新颖的方法，它利用自动化机器学习（AUTOML）技术来减轻偏见。我们的方法包括两个关键的创新：一种新颖的优化功能和公平感知的搜索空间。通过改善汽车的默认优化功能并结合公平目标，我们能够减轻偏见，几乎没有准确性。此外，我们为汽车提出了一种公平感知的搜索空间修剪方法，以减少计算成本和维修时间。我们的方法建立在最先进的自动扫描工具上，旨在减少现实情况下的偏见。为了证明我们的方法的有效性，我们评估了四个公平问题和16个不同ML模型的方法，我们的结果显示了基线和现有偏见缓解技术的显着改善。我们的公平AUTOML的方法成功修复了64例货物中的60个，而现有的缓解技术仅修复了64例中的44例。
Machine learning (ML) is increasingly being used in critical decision-making software, but incidents have raised questions about the fairness of ML predictions. To address this issue, new tools and methods are needed to mitigate bias in ML-based software. Previous studies have proposed bias mitigation algorithms that only work in specific situations and often result in a loss of accuracy. Our proposed solution is a novel approach that utilizes automated machine learning (AutoML) techniques to mitigate bias. Our approach includes two key innovations: a novel optimization function and a fairness-aware search space. By improving the default optimization function of AutoML and incorporating fairness objectives, we are able to mitigate bias with little to no loss of accuracy. Additionally, we propose a fairness-aware search space pruning method for AutoML to reduce computational cost and repair time. Our approach, built on the state-of-the-art Auto-Sklearn tool, is designed to reduce bias in real-world scenarios. In order to demonstrate the effectiveness of our approach, we evaluated our approach on four fairness problems and 16 different ML models, and our results show a significant improvement over the baseline and existing bias mitigation techniques. Our approach, Fair-AutoML, successfully repaired 60 out of 64 buggy cases, while existing bias mitigation techniques only repaired up to 44 out of 64 cases.