Summary of In Defence Of Post-hoc Explainability, by Nick Oh
In Defence of Post-hoc Explainability
by Nick Oh
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 research paper introduces a new philosophical framework for machine learning called Computational Interpretabilism (CI), which aims to resolve the tension between model opacity and scientific understanding. The authors argue that while some models may be intrinsically interpretable, post-hoc interpretability is essential in scientific AI applications. They propose CI as a way to establish structured model interpretation through empirical validation, rather than requiring complete transparency. This approach allows for epistemically justified insights without sacrificing model complexity. The authors demonstrate the effectiveness of their method using mediated understanding and bounded factivity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem in machine learning. Right now, scientists are using AI models that can be very good at doing certain tasks, but they don’t really understand how these models work or what’s going on inside them. The authors of this paper propose a new way of thinking about how we use AI, called Computational Interpretabilism (CI). They say that instead of trying to make the models completely transparent, we can still get useful insights by analyzing and understanding how they work after they’ve been trained. This approach is important for scientists because it helps them trust the results they’re getting from these complex models. |
Keywords
» Artificial intelligence » Machine learning
