Summary of Explaining Text Classifiers with Counterfactual Representations, by Pirmin Lemberger et al.
Explaining Text Classifiers with Counterfactual Representations
by Pirmin Lemberger, Antoine Saillenfest
First submitted to arxiv on: 1 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 proposed method generates counterfactuals by intervening in text representations to overcome challenges in constructing realistic hypothetical events. The approach is motivated by the need to explain classifier decisions and mitigate biases. By leveraging Pearl’s causal inference framework, the method ensures minimal disruption and theoretical soundness. Experiments on synthetic and realistic datasets demonstrate the effectiveness of the proposed approach in generating plausible counterfactuals that can be used for explaining classifiers and bias mitigation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make computer programs explain themselves better. Sometimes these programs, called classifiers, make mistakes because they don’t understand how things would have been different if one thing had changed. The authors came up with a simple idea: instead of trying to change the real world, we can just adjust the words that computers use to describe things. This helps us create fake scenarios (called counterfactuals) that are more like what really happens in the world. They tested this method on some data and showed it works well for helping classifiers explain themselves and reduce unfairness. |
Keywords
* Artificial intelligence * Inference
