Fairlearn: A toolkit for assessing and improving fairness in AI
Fairlearn: A toolkit for assessing and improving fairness in AI
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Fairlearn：用于评估和提高人工智能公平性的工具包
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Kathleen Walker
中科院分区：
文献类型：
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作者：
Sarah Bird;Miroslav Dudík;R. Edgar;Brandon Horn;Roman Lutz;Vanessa Milan;M. Sameki;Hanna M. Wallach;Kathleen Walker
我们介绍了一个开源工具包，它使数据科学家和开发人员能够评估和改善其AI系统的公平性。在公平和模型性能之间，我们强调了AI系统中的公平性是一个社会技术的挑战。不公平的来源 - 某些社会和一些技术 - 不可能完全“ debias”一个系统或保证公平的目标；缓解算法和可视化功能，我们希望它将由利益相关者的潜水员社区塑造，从数据科学家，开发人员和业务决策者到生命的人可能会受到AI系统预测的影响。
We introduce Fairlearn, an open source toolkit that empowers data scientists and developers to assess and improve the fairness of their AI systems. Fairlearn has two components: an interactive visualization dashboard and unfairness mitigation algorithms. These components are designed to help with navigating trade-offs between fairness and model performance. We emphasize that prioritizing fairness in AI systems is a sociotechnical challenge. Because there are many complex sources of unfairness—some societal and some technical—it is not possible to fully “debias” a system or to guarantee fairness; the goal is to mitigate fairness-related harms as much as possible. As Fairlearn grows to include additional fairness metrics, unfairness mitigation algorithms, and visualization capabilities, we hope that it will be shaped by a diverse community of stakeholders, ranging from data scientists, developers, and business decision makers to the people whose lives may be affected by the predictions of AI systems.