可解释的算法法庭医学
Brandon L Garrett1,2, Cynthia Rudin3,4,5,6,7
1School of Law, Duke University School of Law, Durham, NC 27708.
概括
执法部门执法执法
科学领域:
- 法医科学 法医科学 法医科学
- 计算机科学 计算机科学
- 刑事司法 刑事司法 刑事司法
背景情况:
- 在刑事调查中越来越多地依赖"黑子"人工智能 (AI).
- 人工智能应用包括DNA混合解释,面部识别和复发风险评估.
- 人们对这些AI系统的不透明性和可理解性存在担忧.
研究的目的:
- 挑战复杂,不透明的人工智能能产生更准确的法医证据的假设.
- 探索可解释的"玻璃盒"AI在刑事司法中的潜力.
- 检查在法律程序中使用不透明的人工智能的法律和政策影响.
主要方法:
- 对可解释人工智能 (玻璃盒人工智能) 的计算机科学研究进行审查.
- 在刑事司法背景下分析AI性能.
- 检查黑子AI的法律和宪法影响.
主要成果:
- 与普遍认为的相反,黑子AI并不一定会产生更准确的法医证据.
- 可解释的"玻璃盒"人工智能可以比不透明的替代品更准确.
- 黑子人工智能系统在刑事司法环境中表现更差.
结论:
- 在刑事司法方面,人工智能的"黑子"性能神话被揭穿.
- 可解释的法医AI存在宪法和公共安全利益.
- 政府必须证明在刑事案件中使用黑子AI的合理性;倡导对可解释AI的权利.
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