The Explabox: Model-Agnostic Machine Learning Transparency & Analysis
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912130988507136 |
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| author | Robeer, Marcel Bron, Michiel Herrewijnen, Elize Hoeseni, Riwish Bex, Floris |
| author_facet | Robeer, Marcel Bron, Michiel Herrewijnen, Elize Hoeseni, Riwish Bex, Floris |
| contents | 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/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_15257 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Explabox: Model-Agnostic Machine Learning Transparency & Analysis Robeer, Marcel Bron, Michiel Herrewijnen, Elize Hoeseni, Riwish Bex, Floris Machine Learning Artificial Intelligence Software Engineering I.2; D.2.5 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/. |
| title | The Explabox: Model-Agnostic Machine Learning Transparency & Analysis |
| topic | Machine Learning Artificial Intelligence Software Engineering I.2; D.2.5 |
| url | https://arxiv.org/abs/2411.15257 |