论文标题
审核ML模型，以实现个人偏见和不公平
Auditing ML Models for Individual Bias and Unfairness
论文作者
论文摘要
我们考虑审核ML模型的单个偏见/不公平的任务。我们在优化问题中正式将任务形式化，并为最佳价值开发了一套推论工具。我们的工具使我们能够获得涵盖目标/控制I类错误率的目标/控制的渐近置信区间和假设测试。为了证明我们的工具的实用性，我们使用它们来揭示Northpointe的Compas Recidivism预测工具中的性别和种族偏见。
We consider the task of auditing ML models for individual bias/unfairness. We formalize the task in an optimization problem and develop a suite of inferential tools for the optimal value. Our tools permit us to obtain asymptotic confidence intervals and hypothesis tests that cover the target/control the Type I error rate exactly. To demonstrate the utility of our tools, we use them to reveal the gender and racial biases in Northpointe's COMPAS recidivism prediction instrument.
