Trust in User-generated Evidence: Analysing the Impact of Deepfakes on Accountability Processes for Human Rights Violations (TRUE)

信任用户生成的证据：分析 Deepfakes 对侵犯人权行为问责流程的影响 (TRUE)

• 批准号: EP/X016021/1
• 负责人: Yvonne McDermott
• 金额: $ 164.71万
• 依托单位: Swansea University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX016021%2F1
• 关键词: Trust; User; generated; Evidence; Analysing

User-generated evidence - defined as information recorded by an ordinary citizen and used in legal adjudication - plays anincreasingly important role in accountability processes. Across the world, advances in mobile phone technology and increasinginternet access mean that millions of important photographs and videos depicting mass human rights violations have been, and willcontinue to be, created and shared online. Mass atrocity trials in Sweden, Germany, The Netherlands, and the International CriminalCourt, amongst others, have already utilised this kind of evidence, as have UN Human Rights Council-mandated commissions ofinquiry, fact-finding missions, and investigations. Yet, at the same time, the public is increasingly confronted with examples ofdeepfakes - hyper-realistic images, videos, or audio recordings created using machine learning technology - which are only likely tobecome more advanced and difficult to detect as the technology progresses. These two developments pose an importantconundrum: have perceptions of deepfakes led to a mistrust in user-generated evidence? And if so, what does that mean for the roleof such evidence in future human rights accountability processes? Much of the literature to date has expressed a concern that the risein deepfakes will lead to mass mistrust in user-generated evidence, and that this in turn will decrease its epistemic value in legalproceedings. This may well be the case, but no study has yet tested that assumption. This is a major evidence gap that urgently needsto be addressed. Through an innovative interdisciplinary methodology at the intersection of law, psychology, and linguistics, thispioneering project will develop the first systematic account of trust in user-generated evidence, in the specific context of its use inhuman rights accountability processes.

用户生成的证据--被定义为普通公民记录并用于法律的裁决的信息--在问责过程中发挥着越来越重要的作用。在世界各地，移动的电话技术的进步和互联网接入的增加意味着数百万张描述大规模侵犯人权行为的重要照片和视频已经并将继续在网上创建和分享。瑞典、德国、荷兰和国际刑事法院等国的大规模暴行审判已经使用了这类证据，联合国人权理事会授权的调查委员会、实况调查团和调查也是如此。然而，与此同时，公众越来越多地面临深度伪造的例子--使用机器学习技术创建的超现实图像、视频或录音--随着技术的进步，它们只会变得更加先进，难以检测。这两个发展提出了一个重要的难题：对deepfakes的看法是否导致了对用户生成证据的不信任？如果是，这对此类证据在未来人权问责进程中的作用意味着什么？迄今为止，许多文献都表示担心，deepfakes的兴起将导致对用户生成的证据的大规模不信任，这反过来又会降低其在法律诉讼中的认识价值。这很可能是事实，但目前还没有任何研究来验证这一假设。这是一个急需解决的重大证据缺口。通过法律，心理学和语言学交叉的创新跨学科方法，这个开创性的项目将在其使用非人道权利问责过程的特定背景下，开发第一个对用户生成证据信任的系统性说明。