Summary of Interpretability Needs a New Paradigm, by Andreas Madsen et al.
Interpretability Needs a New Paradigm
by Andreas Madsen, Himabindu Lakkaraju, Siva Reddy, Sarath Chandar
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 explores the concept of interpretability in machine learning, focusing on explaining complex models to humans. It discusses two prevailing paradigms: intrinsic, where models are designed to be explained, and post-hoc, where black-box models can be interpreted. The debate centers around ensuring faithfulness, as false explanations can lead to overconfidence in AI systems. This paper advocates for considering new paradigms while prioritizing faithfulness. By examining the history of scientific paradigms and their underlying beliefs, limitations, and values, the authors present three emerging interpretability paradigms: designing models with measurable faithfulness, optimizing models for faithful explanations, and developing models that generate both predictions and explanations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making AI systems understandable to humans. It looks at two ways we try to do this: one where the model is designed to be explained, and another where complex models can be interpreted after they’ve made a prediction. The problem is that if our explanations are not true reflections of how the model works, people might have too much confidence in AI. This paper suggests looking at new approaches while making sure our explanations are accurate. By learning from how science has changed over time, the authors present three new ways to make AI more understandable. |
Keywords
» Artificial intelligence » Machine learning
