论文标题
刑事司法系统的计算机视觉应用中的偏见和公平性
Bias and Fairness in Computer Vision Applications of the Criminal Justice System
论文作者
论文摘要
在过去的几年中，涉及AI驱动警察工作的歧视性实践一直是许多争议的主题，Compas，Predpol和Shotspotter等算法被指控不公平地影响少数群体。同时，机器学习中的公平性，尤其是计算机视觉中的问题，已经成为越来越多的学术工作的主题。在本文中，我们研究了这些区域如何相交。我们提供有关这些实践如何存在的信息以及减轻它们的困难。然后，我们检查目前正在开发的三个应用程序，以了解它们对公平性构成的风险以及如何减轻这些风险。
Discriminatory practices involving AI-driven police work have been the subject of much controversies in the past few years, with algorithms such as COMPAS, PredPol and ShotSpotter being accused of unfairly impacting minority groups. At the same time, the issues of fairness in machine learning, and in particular in computer vision, have been the subject of a growing number of academic works. In this paper, we examine how these area intersect. We provide information on how these practices have come to exist and the difficulties in alleviating them. We then examine three applications currently in development to understand what risks they pose to fairness and how those risks can be mitigated.
