[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123534-en":3,"doc-seo-123534-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123534,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","THE Explabox - MODEL-AGNOSTIC MACHINE LEARNING TRANSPARENCY & ANALYSIS","Explabox is an open-source Python toolkit for transparent and responsible machine learning model development and usage. It supports explainable, fair, robust, and auditable outcomes through a four-step workflow: explore, examine, explain, and expose. The toolkit converts complex models and data into interpretable “digestibles” covering descriptive statistics, performance metrics, local and global behavior explanations, and assessments for robustness, security, and fairness. Built for easy integration with existing datasets and models, it targets text data and models initially and enables reproducible, stakeholder-friendly communication of results.","arXiv :2411 . 15257v1 [ cs .LG] 22 Nov 2024  \nTHE Explabox: MODEL-AGNOSTIC MACHINE LEARNING TRANSPARENCY & ANALYSIS  \n Marcel Robeer 1 ,2 ∗  Michiel Bron 1 ,2  Elize Herrewijnen 1 ,2 Riwish Hoeseni2  Floris Bex 1 ,3  \n1National Police Lab AI, Utrecht University, The Netherlands  \n2Netherlands National Police, The Netherlands  \n3Tilburg Institute for Law, Technology and Society, Tilburg University, The Netherlands  \nABSTRACT  \nWe 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.  \nImplemented 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.  \nExplabox’s code and documentation are available open-source at [https://explabox.readthedocs.io](https://explabox.readthedocs.io). Keywords explainable AI (XAI) · interpretability · fairness · robustness · AI safety · auditability  \n1 Introduction  \nIt is crucial that Machine Learning (ML) development and usage is done in a responsible and transparent manner. Highstakes organizational decisions may significantly impact individuals and society, with potential severe consequences stemming from biases or model errors. This is exemplified by the EU AI Act’s regulatory framework, requiring high-risk systems to be properly tested, documented and assessed on their conformity before being applied in practice [Edwards, 2022] . Yet, operationalizing transparency (explainable ML) and testing model behavior (fairness, robustness, and security auditing) remains a difficult and laborious task, given the myriad of techniques and their associated learning curves. In response, we have devised a comprehensive four-step analysis strategy—explore, examine, explain, and expose—ensuring holistic model transparency and testing. The open-source Explabox offers these analyses through well-documented, reproducible steps. Data scientists can now access a unified, model-agnostic approach developed and used in a high-stakes context—the Netherlands National Police—that is applicable to any text classifier or regressor. We have developed the Explabox in an organizational environment where models and data are analyzed repeatedly, and where internal and external stakeholders have varying explanatory needs and preferred formats. Several related tools have been made available, such as AIX360 [Arya et al., 2019], alibi explain [Klaise et al., 2021], dalex [Banieckiet al., 2021], CheckList [Ribeiro et al., 2020], and AIF360 [Bellamy et al., 2018] . However, these tools exhibit shortcomings such as incompatibility with recent Python versions (3.8–3.12), restricted software functionality primarily focused on testing or explainability, an absence of reproducible outcomes, or that they do not provide the flexibility regarding how results can be communicated to address stakeholder needs. To fill this gap, we propose the Explabox.  \nThe Explabox is an open-source Python toolkit that supports organizations with responsible ML development with minimal disruption to practitioners’workflows through a four-step analysis strategy easily embedded with existing datasets and models, and providing a central node for connecting with state-of-the-art research and sharing best practices.  \n∗ [Corresponding author: m.j.robeer@uu.nl](Corresponding author: m.j.robeer@uu.nl).  \n2 The Explabox: Explor","cbCaiv2hjnjlG0ns","https://ap.wps.com/l/cbCaiv2hjnjlG0ns","pdf",885948,1,5,"English","en",105,"# Introduction\n## Responsible and transparent ML\n## Limitations of existing tools\n# The Explabox: Explore, Examine, Explain & Expose your ML models\n## Four-step analyses and digestibles\n## Ingestibles\n# Explore the data and examine model behavior\n## Wrap model & data\n## Explore data distributions\n## Examine wrongly classified examples\n## Explain local/global behavior\n## Expose metric sensitivity and perturbations","[{\"question\":\"What is Explabox and what problem does it address?\",\"answer\":\"Explabox is an open-source toolkit that helps organizations develop and use machine learning models transparently and responsibly. It targets the difficulty and labor of operationalizing explainability and testing fairness, robustness, and security behaviors.\"},{\"question\":\"How does Explabox structure its analysis workflow?\",\"answer\":\"Explabox uses a four-step strategy: explore, examine, explain, and expose. These steps produce model-agnostic outputs that convert complex models and data into interpretable digestibles for auditing and understanding.\"},{\"question\":\"What kinds of insights and assessments does Explabox provide?\",\"answer\":\"It generates digestibles for descriptive statistics, performance metrics, local and global behavior explanations, and evaluations of robustness, security, and fairness. This supports explainability and auditability of ML systems.\"}]","THE Explabox - MODEL-AGNOSTIC MACHINE LEARNING TRANSPARENCY & ANALYSIS | PDF",1785817186,13,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"the-explabox-model-agnostic-machine-learning-transparency-analysis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-explabox-model-agnostic-machine-learning-transparency-analysis/123534/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is Explabox and what problem does it address?","Question",{"text":75,"@type":76},"Explabox is an open-source toolkit that helps organizations develop and use machine learning models transparently and responsibly. It targets the difficulty and labor of operationalizing explainability and testing fairness, robustness, and security behaviors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does Explabox structure its analysis workflow?",{"text":80,"@type":76},"Explabox uses a four-step strategy: explore, examine, explain, and expose. These steps produce model-agnostic outputs that convert complex models and data into interpretable digestibles for auditing and understanding.",{"name":82,"@type":73,"acceptedAnswer":83},"What kinds of insights and assessments does Explabox provide?",{"text":84,"@type":76},"It generates digestibles for descriptive statistics, performance metrics, local and global behavior explanations, and evaluations of robustness, security, and fairness. 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