Speculating on Risks of AI Clones to Selfhood and Relationships: Doppelganger-phobia, Identity Fragmentation, and Living Memories

Authors
Lee, Patrick Yung KangMa, Ning F.Kim, Ig-JaeYoon, Dongwook
Issue Date
2023-04
Publisher
Association for Computing Machinery (ACM)
Citation
Proceedings of the ACM on Human-Computer Interaction, v.7, no.CSCW1, pp.1 - 28
Abstract
Digitally replicating the appearance and behaviour of individuals is becoming feasible with recent advancements in deep-learning technologies such as interactive deepfake applications, voice conversion, and virtual actors. Interactive applications of such agents, termed AI clones, pose risks related to impression management, identity abuse, and unhealthy dependencies. Identifying concerns AI clones will generate is a prerequisite to establishing the basis of discourse around how this technology will impact a source individual's selfhood and interpersonal relationships. We presented 20 participants of diverse ages and backgrounds with 8 speculative scenarios to explore their perception towards the concept of AI clones. We found that (1. doppelganger-phobia) the abusive potential of AI clones to exploit and displace the identity of an individual elicits negative emotional reactions; (2. identity fragmentation) creating replicas of a living individual threatens their cohesive self-perception and unique individuality; and (3. living memories) interacting with a clone of someone with whom the user has an existing relationship poses risks of misrepresenting the individual or developing over-attachment to the clone. These findings provide an avenue to discuss preliminary ethical implications, respect for identity and authenticity, and design recommendations for creating AI clones.
Keywords
AIclones; identity; interpersonalrelationship; impressionmanagement; self-hood; AIagents; machinelearningapplications; human-AIinteraction; human-centeredAI; doppelgangerphobia; identifyfragmentation; livingmemories; risks
URI
https://pubs.kist.re.kr/handle/201004/75747
DOI
10.1145/3579524
Appears in Collections:
KIST Article > 2023
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