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

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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.