Summary of Active Fourier Auditor For Estimating Distributional Properties Of Ml Models, by Ayoub Ajarra et al.
Active Fourier Auditor for Estimating Distributional Properties of ML Models
by Ayoub Ajarra, Bishwamittra Ghosh, Debabrota Basu
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (stat.ML)
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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 A Machine Learning (ML) model auditing framework is proposed to verify and audit three essential ML model properties: robustness, individual fairness, and group fairness. The framework, dubbed Active Fourier Auditor (AFA), estimates these properties without parametrically reconstructing the target ML model. AFA queries sample points based on the Fourier coefficients of the ML model and provides high probability error bounds on its estimates. Numerical experiments demonstrate that AFA is more accurate and sample-efficient than baselines on various datasets and models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to check if Machine Learning (ML) models are working correctly is developed. This approach, called Active Fourier Auditor (AFA), looks at the properties of ML models without rebuilding them from scratch. The goal is to ensure that ML models are fair and work well in different situations. AFA estimates these properties by picking specific data points based on the model’s characteristics. This method is shown to be more accurate and efficient than previous methods. |
Keywords
» Artificial intelligence » Machine learning » Probability
