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Know thyself, improve thyself: personalized LLMs for self-knowledge and moral enhancement

Abstract:
In this paper, we suggest that personalized LLMs trained on information written by or otherwise pertaining to an individual could serve as artificial moral advisors (AMAs) that account for the dynamic nature of personal morality. These LLM-based AMAs would harness users’ past and present data to infer and make explicit their sometimes-shifting values and preferences, thereby fostering self-knowledge. Further, these systems may also assist in processes of self-creation, by helping users reflect on the kind of person they want to be and the actions and goals necessary for so becoming. The feasibility of LLMs providing such personalized moral insights remains uncertain pending further technical development. Nevertheless, we argue that this approach addresses limitations in existing AMA proposals reliant on either predetermined values or introspective self-knowledge.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/s11948-024-00518-9

Authors


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Institution:
University of Oxford
Division:
HUMS
Department:
Philosophy
Research group:
Uehiro Oxford Institute and Wellcome Centre for Ethics and Humanities
Role:
Author
ORCID:
0000-0001-5163-3017
More by this author
Institution:
University of Oxford
Division:
HUMS
Department:
Uehiro Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
HUMS
Department:
Uehiro Institute
Role:
Author
ORCID:
0000-0001-9691-2888
More by this author
Institution:
University of Oxford
Division:
HUMS
Department:
Uehiro Institute
Role:
Author


More from this funder
Funder identifier:
https://ror.org/029chgv08
Grant:
203132/Z/16/Z


Publisher:
Springer Nature
Journal:
Science and Engineering Ethics More from this journal
Volume:
30
Issue:
6
Article number:
54
Publication date:
2024-11-21
Acceptance date:
2024-10-10
DOI:
EISSN:
1471-5546
ISSN:
1353-3452


Language:
English
Keywords:
Pubs id:
2037599
Local pid:
pubs:2037599
Deposit date:
2024-10-10

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