Summary of Automated Trustworthiness Testing For Machine Learning Classifiers, by Steven Cho and Seaton Cousins-baxter and Stefano Ruberto and Valerio Terragni
Automated Trustworthiness Testing for Machine Learning Classifiers
by Steven Cho, Seaton Cousins-Baxter, Stefano Ruberto, Valerio Terragni
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Software Engineering (cs.SE)
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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 trustworthiness in Machine Learning (ML) models, which is crucial for their reliable performance on unseen data. The authors highlight that ML’s widespread adoption in critical domains like finance, healthcare, and transportation necessitates evaluating not only predictive accuracy but also the reasons behind predictions. To achieve this, explainable techniques such as LIME and SHAP have been developed to provide insights into ML models’ decision-making processes. However, assessing the plausibility of these explanations remains a challenge, with current approaches relying on human judgment. The paper aims to address this limitation by [insert details about the approach or methodology]. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is really important for things like medicine and finance. But how can we be sure that the computer programs making decisions are doing it correctly? This paper talks about “trustworthiness” – making sure the programs understand why they’re making certain predictions. Right now, there are special techniques to explain what these programs are thinking (like LIME or SHAP). But someone has to look at those explanations and decide if they make sense. This paper is trying to figure out a way to do that automatically, so we can trust the computer programs more. |
Keywords
» Artificial intelligence » Machine learning
