可解释的AI

Kelly E. Carter
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引用次数: 0

摘要

使用深度学习的人工智能识别模式,并以一种没有明确编程的方式将答案关联起来,这既带来了更高的风险,也带来了更高的回报。2016年,ProPublica对替代制裁的惩教罪犯管理分析(COMPAS)算法进行了曝光。该算法被刑事司法官员用来确定累犯率。这是加州、纽约、威斯康辛州和佛罗里达州部分地区使用的几种算法之一。COMPAS算法被发现有明显的种族偏见,这与实际的累犯率无关。根据佛罗里达州布劳沃德县的数据,ProPublica断言,COMPAS对暴力犯罪的预测准确率为20%,黑人被贴上高风险标签的几率几乎是白人的两倍。这项研究受到了广泛的批评,包括智库社区正义资源对数据和主题的误解。无论如何,对人工智能结果的偏见和信任问题仍然存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
40 xAI: eXplainable AI
AI that uses deep learning identifies patterns and correlates answers in a way that is not explicitly programed, creating both higher risks and rewards. In 2016, an exposé by ProPublica was done on the Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) algorithm. This algorithm is used by criminal justice officials to determine recidivism rates. This is one of several algorithms used by California, New York, Wisconsin, and parts of Florida. The COMPAS algorithm was found to have significant racial bias, which was not correlated to actual recidivism rates. Using data from Broward County, Florida ProPublica asserted that COMPAS predictions of violent crimes were correct 20 percent of the time, and that blacks were labeled at higher risk almost twice as much as whites. This study was widely criticized, including by the think tank Community Resources for Justice for misinterpretations of the data and the subject matter. Regardless, the problem of bias and trust in AI outcomes still exists.
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