Summary of T-explainer: a Model-agnostic Explainability Framework Based on Gradients, by Evandro S. Ortigossa et al.
T-Explainer: A Model-Agnostic Explainability Framework Based on Gradients
by Evandro S. Ortigossa, Fábio F. Dias, Brian Barr, Claudio T. Silva, Luis Gustavo Nonato
First submitted to arxiv on: 25 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 tackles the opacity of machine learning models by developing Explainable Artificial Intelligence (XAI) approaches that provide understandable explanations for complex predictions. The authors focus on feature attribution/importance methods, which determine the significance of input features in the prediction process. However, existing methods have limitations, such as instability, leading to divergent explanations. To address this challenge, the paper introduces T-Explainer, a novel local additive attribution explainer based on Taylor expansion. This method has desirable properties like local accuracy and consistency, making it stable over multiple runs. The authors demonstrate T-Explainer’s effectiveness in benchmark experiments against well-known attribution methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how machines learn by explaining why they make certain predictions. Right now, many machine learning models are like black boxes, making it hard for people to figure out why they work the way they do. The authors want to change this by developing a new approach called Explainable Artificial Intelligence (XAI). They’re especially interested in feature attribution, which shows how important each piece of information is when making a prediction. The problem is that most methods have problems, like giving different answers even for the same data. To solve this, they created T-Explainer, a new way to explain why machines make predictions. It’s more accurate and consistent than other methods. |
Keywords
» Artificial intelligence » Machine learning
