Speculating on Risks of AI Clones to Selfhood and Relationships: Doppelganger-phobia, Identity Fragmentation, and Living Memories
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".