Summary of Learning to Guide Human Decision Makers with Vision-language Models, by Debodeep Banerjee et al.
Learning To Guide Human Decision Makers With Vision-Language Models
by Debodeep Banerjee, Stefano Teso, Burcu Sayin, Andrea Passerini
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel framework called Learning to Guide (LTG) that addresses the limitations of mainstream approaches in developing AIs for assisting human decision-making in high-stakes tasks. LTG shifts the focus from machine-driven decision-making to guidance provision, allowing humans to take full responsibility for making decisions. The authors introduce SLOG, an approach that leverages human feedback to transform vision-language models into interpretable and task-specific textual guidance generators. Empirical evaluation on a real-world medical diagnosis task demonstrates the promise of LTG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about finding a better way to work with artificial intelligence in high-pressure situations, like diagnosing diseases. Right now, we usually use AI to help us make decisions, but that can be bad because it might lead us to rely too much on the machine and not enough on our own judgment. This new approach, called Learning to Guide, is different. It lets machines provide guidance instead of making decisions for us, so we can still use our own expertise. The authors also came up with a way to make this guidance more understandable and useful by using human feedback. |
