Summary of Visual Evaluative Ai: a Hypothesis-driven Tool with Concept-based Explanations and Weight Of Evidence, by Thao Le et al.
Visual Evaluative AI: A Hypothesis-Driven Tool with Concept-Based Explanations and Weight of Evidence
by Thao Le, Tim Miller, Ruihan Zhang, Liz Sonenberg, Ronal Singh
First submitted to arxiv on: 13 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
GrooveSquid.com Paper Summaries
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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 This paper introduces Visual Evaluative AI, a decision-support tool that extracts positive and negative evidence from images to inform hypotheses. The system identifies high-level human concepts in an image and calculates the Weight of Evidence (WoE) for each hypothesis, facilitating informed decision-making. In the skin cancer domain, the authors develop a web-based application allowing users to upload dermatoscopic images, select hypotheses, and analyze their decisions by evaluating provided evidence. The paper also explores the effectiveness of Visual Evaluative AI across different concept-based explanation approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand what’s happening in pictures and use that understanding to make good decisions. It’s like a detective tool that looks at an image and says, “Ah, I see something here that makes me think this hypothesis is true or false.” The tool is tested on skin cancer images, where doctors can upload pictures and get help deciding if a mole might be cancerous. It’s also useful for other situations where we want to use pictures to make informed decisions. |
