Summary of Trust Regions For Explanations Via Black-box Probabilistic Certification, by Amit Dhurandhar et al.
Trust Regions for Explanations via Black-Box Probabilistic Certification
by Amit Dhurandhar, Swagatam Haldar, Dennis Wei, Karthikeyan Natesan Ramamurthy
First submitted to arxiv on: 17 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces a novel problem in machine learning explainability certification, focusing on probabilistic black box models with query access. The goal is to find the largest hypercube centered at an example where applying explanations within this region meets certain quality criteria, such as fidelity greater than a specific value. This trust region has multiple benefits, including insight into model behavior, ascertained stability of explanations, explanation reuse, and a meta-metric for comparing methods. The paper formally defines this problem, proposes solutions with theoretical guarantees, and experimentally evaluates their effectiveness on synthetic and real data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new way to understand how machine learning models work by creating a “trust region” around individual predictions. This means finding the largest area where the model’s explanations are reliable and consistent. This has many advantages, including giving us insight into how the model works in specific situations, making it more stable and reusable, and providing a way to compare different explanation methods. |
Keywords
* Artificial intelligence * Machine learning
