The Explabox: Model-Agnostic Machine Learning Transparency & Analysis

• URL: http://arxiv.org/abs/2411.15257v1
• Date: Fri, 22 Nov 2024 09:10:57 GMT
• Title: The Explabox: Model-Agnostic Machine Learning Transparency & Analysis
• Authors: Marcel Robeer, Michiel Bron, Elize Herrewijnen, Riwish Hoeseni, Floris Bex,
• Abstract summary: We present the Explabox: an open-source toolkit for transparent and responsible machine learning (ML) model development and usage. It aids in achieving explainable, fair and robust models by employing a four-step strategy: explore, examine, explain and expose. The toolkit encompasses digestibles for descriptive statistics, performance metrics, model behavior explanations (local and global) and robustness, security, and fairness assessments.
• Score: 1.9864651310779593
• License:
• Abstract: We present the Explabox: an open-source toolkit for transparent and responsible machine learning (ML) model development and usage. Explabox aids in achieving explainable, fair and robust models by employing a four-step strategy: explore, examine, explain and expose. These steps offer model-agnostic analyses that transform complex 'ingestibles' (models and data) into interpretable 'digestibles'. The toolkit encompasses digestibles for 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. It empowers model developers and testers to operationalize explainability, fairness, auditability, and security. The initial release focuses on text data and models, with plans for expansion. Explabox's code and documentation are available open-source at https://explabox.readthedocs.io/.

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