As the concept of artificial moral advisors (AMAs) gains attention, researchers are exploring the possibility of using Large Language Models (LLMs) to provide personalized moral guidance. A new study suggests that LLMs could be used to create AMAs that account for an individual's dynamic morality, values, and preferences by training on information written by or pertaining to them. This approach addresses limitations in existing AMA proposals reliant on predetermined values or introspective self-knowledge.
By harnessing users' past and present data, these systems may also assist in processes of self-creation, helping users reflect on the kind of person they want to be and the actions necessary for becoming that person. However, the feasibility of using LLMs as AMAs remains uncertain pending further technical development, with challenges including ensuring accuracy and reliability, addressing bias and fairness concerns, and developing effective evaluation methods.
The study highlights the limitations of existing AMA proposals, which often rely on predetermined values or introspective self-knowledge, failing to account for the dynamic nature of personal morality. The potential implications of using AI life assistants capable of handling various professional and personal tasks are significant, raising questions around regulation, oversight, and potential impact on human relationships and social dynamics.
The study suggests that LLMs may offer a more nuanced and context-dependent approach to moral guidance by providing personalized moral insights based on users' data. While the idea is promising, further technical development is needed to ensure the accuracy and reliability of LLM outputs and address concerns around bias and fairness in AI decision-making.
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