Building trust, an LLM thought experiment (lucaskostka.com)

A recent thought experiment tackles the challenge of building trust in language models (LLMs) by imagining a scenario where a coder encounters a newly invented programming language with no documentation. The piece emphasizes that trust in AI is dynamic and should be established through systematic evaluation and data gathering rather than blind faith. The author proposes a structured approach that includes running programs in isolated environments, validating syntax, and performing small programming tasks to assess the LLM's reliability and calibration. This exploration is significant for the AI/ML community as it raises crucial questions about the delegation of control to LLMs. The author argues that trust must be built over time through experience and consistency in the model’s responses, rather than being a blanket acceptance of its autonomy. By framing trust as a probabilistic assessment grounded in structured judgment, the discussion highlights the need for robust data and experimental validation, ultimately suggesting that true autonomy in AI arises not from unconditional faith but from a carefully cultivated relationship based on trust.