A Roadmap for Alignable Algorithmic Decision-Makers in the Medical Triage Domain

Abstract

Artificial intelligence (AI) is increasingly being used in low- and high-stakes decision-making. However, safe and responsible use of AI decision-making systems must also consider human values and characteristics. A promising research direction is to develop novel methods and techniques to align AI systems with human values and intentions, potentially reducing undesirable or harmful behaviors while promoting greater human trust. In this paper, we highlight several promising approaches to this AI alignment problem, focusing on the use of large language models (LLMs) as alignable decision-makers. Specifically, these alignment approaches include several novel prompt-based techniques (using zero- or few-shot learning, persona narratives, or training on a large dataset of pluralistic values) and a technique based on transforming output word embeddings. We demonstrate the feasibility of these approaches for difficult decision-making in the medical triage domain, while also providing several promising future research directions to pursue.

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