Synthetic heritage: Online platforms, deceptive genealogy and the ethics of algorithmically generated memory

Mcavoy, Eva Nieto and Kidd, Jenny ORCID: https://orcid.org/0000-0003-0188-2140 2024. Synthetic heritage: Online platforms, deceptive genealogy and the ethics of algorithmically generated memory. Memory, Mind & Media Item availability restricted.

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Abstract

Services offered by genealogy companies are increasingly underpinned by computational remediation and algorithmic power. Users are encouraged to employ a variety of mobile web and app plug-ins to create progressively more sophisticated forms of synthetic media featuring their (often deceased) ancestors. As the promotion of deepfake and voice-synthesizing technologies intensifies within genealogical contexts – aggrandised as mechanisms for ‘bringing people back to life’ –, we argue it is crucial that we critically examine these processes and the socio-technical infrastructures that underpin them, as well as their mnemonic impacts. In this article we present a study of two AI-enabled services released by the genealogy company MyHeritage: Deep Nostalgia (launched 2020), and DeepStory (2022). We carry out a close critical reading of these services and the outputs they produce which we understand as examples of ‘remediated memory’ (Kidd and Nieto McAvoy 2023) shaped by corporate interests. We examine the distribution of agency where the promotion by these platforms of unique and personalised experiences comes into tension with the propensity of algorithms to homogenise. The analysis intersects with nascent ethical debates about the exploitative and extractive qualities machine learning. Our research unpacks the social and (techno-)material implications of these technologies, demonstrating an enduring individual and collective need to connect with our past(s), and to test and extend our memories and recollections through increasingly intense and proximate new media formats.

Item Type:	Article
Status:	In Press
Schools:	Journalism, Media and Culture
Publisher:	Cambridge University Press
ISSN:	2635-0238
Date of First Compliant Deposit:	17 April 2024
Date of Acceptance:	15 April 2024
Last Modified:	17 Apr 2024 21:40
URI:	https://orca.cardiff.ac.uk/id/eprint/168014

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