Summary of The Explabox: Model-agnostic Machine Learning Transparency & Analysis, by Marcel Robeer et al.
The Explabox: Model-Agnostic Machine Learning Transparency & Analysis
by Marcel Robeer, Michiel Bron, Elize Herrewijnen, Riwish Hoeseni, Floris Bex
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The Explabox is an open-source toolkit designed to facilitate transparent and responsible machine learning (ML) model development and usage. It enables the creation of explainable, fair, and robust models by employing a four-step strategy: explore, examine, explain, and expose. The toolkit provides model-agnostic analyses that convert complex ‘ingestibles’ into interpretable ‘digestibles’, offering insights into descriptive statistics, performance metrics, model behavior explanations (local and global), and robustness, security, and fairness assessments. Implemented in Python, Explabox supports multiple interaction modes and builds on open-source packages, empowering model developers and testers to operationalize explainability, fairness, auditability, and security. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Explabox is a tool that helps make machine learning models more transparent and fair. It’s like a recipe book for making sense of complex computer programs. The toolkit has four steps: explore the data, examine how well the model works, explain what it’s doing, and expose any biases or flaws. This makes it easier to understand how the model is working and whether it’s treating different groups fairly. |
Keywords
* Artificial intelligence * Machine learning
