AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking
<|reference_start|>AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking: Whether a large language model policy is an explicit constitution or an implicit reward model, it is challenging to assess coverage over the unbounded set of real-world situations that a policy must contend with. We introduce an AI policy design process inspired by mapmaking, which has developed tactics for visualizing and iterating on maps even when full coverage is not possible. With Policy Projector, policy designers can survey the landscape of model input-output pairs, define custom regions (e.g., "violence"), and navigate these regions with rules that can be applied to LLM outputs (e.g., if output contains "violence" and "graphic details," then rewrite without "graphic details"). Policy Projector supports interactive policy authoring using LLM classification and steering and a map visualization reflecting the policy designer's work. In an evaluation with 12 AI safety experts, our system helps policy designers to address problematic model behaviors extending beyond an existing, comprehensive harm taxonomy.<|reference_end|>
@article{lam2024ai,
    title={AI Policy Projector: Grounding LLM Policy Design in Iterative Mapmaking},
    author={Michelle S. Lam, Fred Hohman, Dominik Moritz, Jeffrey P. Bigham,
  Kenneth Holstein, Mary Beth Kery},
    journal={arXiv preprint arXiv:2409.18203},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18203},
    primaryClass={cs.HC cs.AI cs.CL cs.LG}
}