帶有偏見的嘈雜的標籤的公平性評估（CS AI）

• 2020 年 4 月 2 日
• 筆記

風險評估工具在全國範圍內廣泛用於在刑事司法系統內為決策提供依據。最近，人們對這種工具是否可能遭受種族偏見的問題投入了大量關注。在這種類型的評估中，一個基本問題是模型的訓練和評估基於變量（逮捕），該變量可能表示更多的集中關注（犯罪）的未觀察到的結果的嘈雜版本。我們提出了一個敏感性分析框架，用於評估跨群體噪聲的假設如何影響作為再犯的預測因素的風險評估模型的預測偏差屬性。我們在兩個真實的現實世界刑事司法數據集上的實驗結果表明，即使是觀察到的標籤中的小偏差，也可能對基於噪聲結果的分析結論提出質疑。

原文題目：Fairness Evaluation in Presence of Biased Noisy Labels

原文： Risk assessment tools are widely used around the country to inform decision making within the criminal justice system. Recently, considerable attention has been devoted to the question of whether such tools may suffer from racial bias. In this type of assessment, a fundamental issue is that the training and evaluation of the model is based on a variable (arrest) that may represent a noisy version of an unobserved outcome of more central interest (offense). We propose a sensitivity analysis framework for assessing how assumptions on the noise across groups affect the predictive bias properties of the risk assessment model as a predictor of reoffense. Our experimental results on two real world criminal justice data sets demonstrate how even small biases in the observed labels may call into question the conclusions of an analysis based on the noisy outcome.

原文作者：Riccardo Fogliato, Max G'Sell, Alexandra Chouldechova

原文地址：https://arxiv.org/abs/2003.13808