Summary of Auditing Local Explanations Is Hard, by Robi Bhattacharjee et al.
Auditing Local Explanations is Hard
by Robi Bhattacharjee, Ulrike von Luxburg
First submitted to arxiv on: 18 Jul 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 The proposed auditing framework aims to ensure the trustworthiness of machine learning algorithm decisions by allowing a third-party auditor or collective of users to query model decisions and local explanations. The auditor pools information received and checks for basic consistency properties, proving upper and lower bounds on the number of queries required for successful auditing. The results show that successful auditing can be challenging, particularly in high-dimensional cases, and highlight the importance of “locality” in explanation providers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning algorithms are used to make decisions in many areas, but these decisions need to be explained so people trust them. In this paper, researchers explore a new way to check if explanations given by machine learning models are trustworthy. They propose an auditing framework where a third-party auditor or group of users can ask questions about the model’s decisions and the reasons behind those decisions. The goal is to make sure the explanations provided are consistent and accurate. The research shows that this process requires a large number of questions, especially in complex situations. This highlights the importance of providing good, detailed explanations rather than just giving a simple answer. |
Keywords
» Artificial intelligence » Machine learning
