Trust in User-generated Evidence (TRUE)

TRUE is a project selected for funding by the European Research Council and funded by a UKRI Frontier Research grant [no. EP/X016021/1] from 2022-2027, which seeks to explore the impact of deepfakes on trust in user-generated evidence in accountability processes for human rights violations. 

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 User-generated evidence  - defined as information recorded by an ordinary citizen and used in legal adjudication - plays an increasingly important role in accountability processes. Across the world, advances in mobile phone technology and increasing internet access mean that millions of important photographs and videos depicting mass human rights violations have been, and will continue to be, created and shared online.

 

Mass atrocity trials in Sweden, Germany, Democratic Republic of the Congo, and the International Criminal Court, amongst others, have already utilised this kind of evidence, as have UN Human Rights Council-mandated commissions of inquiry, fact-finding missions, and investigations. Yet, at the same time, the public is increasingly confronted with examples of deepfakes - hyper-realistic images, videos, or audio recordings created using machine learning technology - which are only likely to become more advanced and difficult to detect as the technology progresses.

 

These two developments pose an important conundrum:

 

Have perceptions of deepfakes led to mistrust in user-generated evidence? And if so, what does that mean for the role of such evidence in future human rights accountability processes?

 

Much of the literature to date has expressed a concern that the rise in deepfakes will lead to mass mistrust in user-generated evidence, and that this in turn will decrease its epistemic value in legal proceedings. This may well be the case, but no study has yet tested that assumption. This is a major evidence gap that urgently needs to be addressed.

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Through an innovative interdisciplinary methodology at the intersection of law, psychology, and linguistics, TRUE will develop the first systematic account of trust in user-generated evidence, in the specific context of its use in human rights accountability processes.