Summary of On Evaluating Explanation Utility For Human-ai Decision Making in Nlp, by Fateme Hashemi Chaleshtori et al.
On Evaluating Explanation Utility for Human-AI Decision Making in NLP
by Fateme Hashemi Chaleshtori, Atreya Ghosal, Alexander Gill, Purbid Bambroo, Ana Marasović
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
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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 challenges the notion that explainability is a valuable tool for improving human-AI collaboration. Despite its promise, there is limited evidence that explanations actually help people in the situations they are intended to address. To settle this debate, researchers need more human-centered evaluations of explanations, but currently, there are no established guidelines for such studies in NLP. The paper highlights the need for novel metrics, tasks, datasets, and models that cater to human-AI teams. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about whether or not explaining things helps people when we use AI together. Some people think it does, but there isn’t enough evidence to prove it. To figure this out, we need to test how well explanations work in real-life situations. However, right now, we don’t have clear rules for doing these kinds of tests in the field of natural language processing (NLP). The paper says that we need new ways to measure how well explanations work and what kind of tasks, data, and AI models are best suited for working with humans. |
Keywords
» Artificial intelligence » Natural language processing » Nlp
