Summary of Towards a Unified Framework For Evaluating Explanations, by Juan D. Pinto et al.
Towards a Unified Framework for Evaluating Explanations
by Juan D. Pinto, Luc Paquette
First submitted to arxiv on: 22 May 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 The paper reviews the current state of interpretability in machine learning (ML) and human-computer interaction (HCI) research communities. It highlights the importance of unified evaluation criteria to bridge the gap between these two communities. The authors propose a framework that considers faithfulness, intelligibility, plausibility, and stability as essential factors for creating useful explanations. They demonstrate their approach using an example of an interpretable neural network predicting learner behavior. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machine learning models more understandable. Two groups of researchers, one focused on engineering and the other on how people interact with computers, have been working on this problem separately. The authors are trying to find common ground between these two groups by proposing a set of criteria for evaluating explanations. They believe that good explanations should be faithful to the model’s predictions, easy to understand, plausible, and stable. They use an example of a neural network predicting learner behavior to illustrate their approach. |
Keywords
» Artificial intelligence » Machine learning » Neural network
