Synthetic Pasts explores automated and algorithmic processes designed to change the structure and behaviour of media fragments from the past, in-so-doing, offering them new kinds of ‘afterlife’. These developments - for example, creating ‘holographic’ resurrections of celebrities, deepfake historical figures, animated photos from archives, or voice assistants powered with our deceased relatives’ voice data - connect viscerally with urgent discussions about what futures AI heralds.

 

While there have always been myriad and competing narratives about the past produced from personal and institutional archives, recent advances in deep learning technologies that change, manipulate, or create new media objects present unprecedented challenges. 

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For the first time Synthetic Pasts asks what values and cultural assumptions are embedded in the systems that produce synthetic pasts, and what ethical challenges they present; for example, in relation to datafication, exploitation, bias, deception and (often posthumous) consent. In-so-doing it speaks to human concerns that reach beyond the digital, such as how we grieve, how we express and understand identity, how we articulate and commemorate the past, and the impacts of disinformation. In centring such ethical ambiguities,

 

Synthetic Pasts recognises the ways algorithms intervene within and are shaped by society and culture, including how they mediate our processes of memory and memorization.