Learning To Guide Human Decision Makers With Vision-Language Models

2024-03-25

arXiv.org

AI Summary

In high-stakes human-AI collaborative medical decision-making, clinicians often over-rely on AI systems, while existing AI assistance suffers from poor interpretability and insufficient task adaptability. Method: This paper proposes the “Learning-to-Guide” (LTG) framework—a human-centered paradigm that preserves ultimate clinical authority and auditability by leveraging vision-language models (VLMs) to generate interpretable, task-specific textual decision guidance—not autonomous decisions. We introduce SLOG (Supervised Learning of Guidance), a lightweight method that efficiently adapts arbitrary VLMs into high-quality guidance generators using minimal human feedback, integrating instruction tuning, human-feedback-driven prompt optimization, and interpretable text generation. Contribution/Results: Evaluated on real-world diagnostic tasks, LTG significantly improves physician diagnostic accuracy and inter-rater consistency while reducing cognitive load—all without compromising human oversight or system controllability.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

There is increasing interest in developing AIs for assisting human decision-making in high-stakes tasks, such as medical diagnosis, for the purpose of improving decision quality and reducing cognitive strain. Mainstream approaches team up an expert with a machine learning model to which safer decisions are offloaded, thus letting the former focus on cases that demand their attention. his separation of responsibilities setup, however, is inadequate for high-stakes scenarios. On the one hand, the expert may end up over-relying on the machine's decisions due to anchoring bias, thus losing the human oversight that is increasingly being required by regulatory agencies to ensure trustworthy AI. On the other hand, the expert is left entirely unassisted on the (typically hardest) decisions on which the model abstained. As a remedy, we introduce learning to guide (LTG), an alternative framework in which - rather than taking control from the human expert - the machine provides guidance useful for decision making, and the human is entirely responsible for coming up with a decision. In order to ensure guidance is interpretable} and task-specific, we develop SLOG, an approach for turning any vision-language model into a capable generator of textual guidance by leveraging a modicum of human feedback. Our empirical evaluation highlights the promise of method on a challenging, real-world medical diagnosis task.

Problem

• AI-assisted decision-making
• doctor-AI collaboration
• information relevance and comprehensibility

Innovation

• Learning-to-Guide (LTG) method
• SLOG system
• Explainable visual language cues