Summary of Maft: Efficient Model-agnostic Fairness Testing For Deep Neural Networks Via Zero-order Gradient Search, by Zhaohui Wang et al.
MAFT: Efficient Model-Agnostic Fairness Testing for Deep Neural Networks via Zero-Order Gradient Search
by Zhaohui Wang, Min Zhang, Jingran Yang, Bojie Shao, Min Zhang
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); 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 proposes a novel method for testing individual fairness in deep learning models, dubbed Model-Agnostic Fairness Testing (MAFT). MAFT is designed to be scalable and applicable to large-scale networks, using lightweight procedures such as gradient estimation and attribute perturbation. Unlike existing white-box methods, MAFT can identify and address discrimination in DL models without requiring knowledge of the specific algorithm or architecture employed. The authors demonstrate that MAFT achieves similar effectiveness to state-of-the-art white-box methods while improving applicability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to check if artificial intelligence models are fair. Right now, there are ways to do this, but they’re not very good and can’t be used with big models. The new method, called MAFT, uses simpler ideas like looking at how the model changes when you adjust some of its inputs. This makes it much better than previous methods and means that people can use it with really large models. It’s important to make sure AI models are fair because they’re starting to be used in important decisions. |
Keywords
» Artificial intelligence » Deep learning
