从理解到证明:基于人工智能的法医证据评估的计算可靠性
Juan M Durán1, David van der Vloed2, Arnout Ruifrok2
1Delft University of Technology, Jaffalaan 5, 2628 BX, Delft, Netherlands.
Forensic science international. Synergy
|September 17, 2024
概括
在法医科学中的人工智能 (AI) 只要其可靠性得到证明,就可以信任. 计算可靠性 (CR) 使用技术,科学和社会指标来证明对语音和面部比较的人工智能输出的信念.
科学领域:
- 科学哲学的哲学科学哲学
- 法医科学 法医科学 法医科学
- 人工智能的人工智能
背景情况:
- 人工智能算法越来越多地用于法医生物识别,但通常作为"黑子"发挥作用.
- 将这些人工智能系统纳入刑事法中提出了重要的伦理,法律和哲学问题.
- 需要一个多学科的辩论来解决AI在法医证据评估中的适用性.
研究的目的:
- 介绍科学哲学对AI在法医证据评估中的使用的观点.
- 引入计算可靠性 (CR) 作为评估AI可靠性的框架.
- 在法律背景下,论证AI输出在语音和面部比较中的合理性.
主要方法:
- 在法医科学中将计算可靠性 (CR) 的框架应用于AI.
- 将相信人工智能可靠性的理由分为三种类型的指标:技术,科学和社会.
- 基于这些指标,分析AI适用于法医语音和面部比较的适用性.
主要成果:
- 在法医语音和面部比较中的AI输出可以在CR下被认为是可靠的.
- 技术指标包括算法验证,法医应用程序验证和基于案例的验证.
- 科学指标包括面孔/声音中固有的识别信息和已建立的法医指标.
- 社会指标包括科学共识和从业人员的专业知识.
结论:
- 计算可靠性为评估AI在法医证据中提供了一个强大的框架,超出了简单的解释性.
- 专家证人可能会优先考虑技术指标,而事实试验者可能会依靠社会指标来获得AI支持的证据.
- 这项研究支持对人工智能驱动的声音和面部法医比较的合理信念.
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