Summary of Pragmatic Auditing: a Pilot-driven Approach For Auditing Machine Learning Systems, by Djalel Benbouzid et al.
Pragmatic auditing: a pilot-driven approach for auditing Machine Learning systems
by Djalel Benbouzid, Christiane Plociennik, Laura Lucaj, Mihai Maftei, Iris Merget, Aljoscha Burchardt, Marc P. Hauer, Abdeldjallil Naceri, Patrick van der Smagt
First submitted to arxiv on: 21 May 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 procedure aims to make a pragmatic step towards a wider adoption of Machine Learning (ML) auditing, extending AI-HLEG guidelines published by the European Commission. The procedure is based on an ML lifecycle model that focuses on documentation, accountability, and quality assurance, serving as a common ground for alignment between auditors and organisations. Two pilots were conducted on real-world use cases from two different organisations, discussing shortcomings of ML algorithmic auditing and future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning systems are changing the world, but they also raise important questions about ethics and fairness. To make sure these systems are transparent and accountable, we need to be able to audit them properly. This requires a special kind of auditing procedure that takes into account the unique characteristics of machine learning algorithms. Our proposal is designed to help organisations conduct audits in a way that aligns with ethical principles. We tested our approach on real-world examples from two different companies and discussed its limitations and future directions. |
Keywords
» Artificial intelligence » Alignment » Machine learning
