[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82907-en":3,"doc-seo-82907-105":29,"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":13,"seo_description":14,"update_tm":27,"read_time":28},82907,8796095461564,"Liam","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Open Problems in AI Incident Governance","AI systems can fail after deployment in ways that pre-deployment safety assessment cannot anticipate, making adequate AI incident governance essential. The work reviews regulatory and independent frameworks covering definitions, taxonomies, monitoring, reporting, and incident analysis, and finds inconsistent practices across these components. These inconsistencies affect incident data types, categorization methods, and the depth, representativeness, and accuracy of downstream analysis. The paper identifies open problems across the incident-governance pipeline and proposes principles with monitoring guidelines and a reporting template to enable implementation.","Open Problems in AI Incident Governance  \nHarleen Kaur Sidhu 1 Rebecca Scholefield 1 Nour Annan 2 Kevin Hernandez 3 Isabel Nieh Hou 4  \nAbdulrahman Alshaikhi 4 Ze Shen Chin 5 6 Rokas Gipikis 5 7  \narXiv :2607 .05 163v 1 [ cs .CY] 6 Jul 2026  \nAbstract  \nAI systems may produce failures after deployment that pre-deployment safety assessments do not anticipate. Managing these failures requires what we refer to as adequate AI incident governance, where having good definitions, taxonomies, monitoring practices, reporting mechanisms, and incident analysis is essential. We examine existing frameworks related to AI incident governance by regulatory bodies and independent efforts, and find that while there are frameworks that describe how individual functions can be performed, thereis a lack of consistency within the aspects of definitions, classification, monitoring, and reporting.  \nThese inconsistencies apply to the types of incident data that is collected and reported, the ways in which they are categorised, and as a result, the depth, representativeness, and accuracy of analysis that can be performed. We identify open problems at each stage of the incident governance pipeline, and find that the absence of standardised monitoring and reporting requirements constitutes a significant gap. To address this, we propose a set of principles supported by concrete monitoring guidelines and a reporting template to facilitate their implementation.  \n1. Introduction  \nSafety assessments conducted prior to the deployment of an artificial intelligence (AI) system, such as model evaluationsand red-teaming, allow testing against anticipated failure modes under controlled settings (Shevlane et al., 2023) . However, real-world deployment may produce failures that these assessments cannot anticipate, for example, due to emergent behaviours, adversarial attacks, and unanticipated  \n1Independent 2 Sorbonne University 3Rice University 4 Columbia University 5AI Standards Lab 6 Oxford Martin AI Governance Initiative 7Vilnius University. Correspondence to: Rokas Gipiˇskis \u003C[rokas@aistandardslab.org](rokas@aistandardslab.org) >.  \nSecond Workshop on Technical AI Governance Research (TAIGR)@ ICML 2026, Seoul, South Korea. 2026. Copyright 2026 by the author(s) .  \nuse cases (O’Brien et al., 2023 ; Shao et al., 2025) .  \nCollecting, reporting, and analysing information about these incidents enables the identification of causal factors, improves accountability, and mitigates future risks of recurrence. Therefore, effective incident governance plays an important role in improving safety and reducing harm caused by AI systems (Wei & Heim, 2026) . Despite this, we found that there is a lack of consistency across the AI incident governance ecosystem, leading to differences in how incidents are defined, categorised, monitored, reported, and analysed. This limits the comparability of individual incidents and, asa result, reduces the effectiveness of analysis and learning across the field.  \nIn this paper, we analyse how definitions, taxonomies, monitoring practices, reporting mechanisms, and incident analysis play a role in AI incident governance. Section 2 examines existing definitions of AI incidents. Section 3 reviews the taxonomies through which incidents are classified. Sections 4 and 5 address monitoring and reporting respectively, where we find the central challenges: the lack of robust monitoring procedures and reporting templates. To address these gaps, we survey corporate monitoring policies (Appendix A), propose monitoring and reporting principles (Appendices Band C), operationalise them as monitoring guidelines (Appendix D), and propose a reporting template (Appendix E) . Finally, Section 6 considers how incident data can support meaningful analysis.  \n2. Definitions  \nThe definition of an AI incident has an effect on what gets monitored, reported, classified, and investigated. For instance, incident repositories based on different definitions may capture","cbCaiqtuj8vWkjQs","https://ap.wps.com/l/cbCaiqtuj8vWkjQs","pdf",305584,1,21,"English","en",105,"# Introduction\n# Definitions\n# Taxonomies\n# Monitoring\n# Reporting\n# Analysis of Incident Data","[{\"question\":\"Why is AI incident governance needed after deployment?\",\"answer\":\"AI systems may fail in deployment scenarios that pre-deployment evaluations and red-teaming cannot anticipate, such as emergent behaviors or adversarial attacks. Incident governance helps manage these failures through structured processes.\"},{\"question\":\"What inconsistency does the paper identify in the current AI incident governance ecosystem?\",\"answer\":\"The paper finds lack of consistency across definitions, classification, monitoring, reporting, and incident analysis. These differences affect what data is collected and how it is interpreted.\"},{\"question\":\"How do the proposed principles aim to address the identified gaps?\",\"answer\":\"The paper proposes principles supported by concrete monitoring guidelines and a reporting template. This is intended to standardize requirements and improve the implementation of incident governance.\"}]",1784183865,53,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"open-problems-in-ai-incident-governance","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/open-problems-in-ai-incident-governance/82907/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-24","2026-07-16",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},"Why is AI incident governance needed after deployment?","Question",{"text":75,"@type":76},"AI systems may fail in deployment scenarios that pre-deployment evaluations and red-teaming cannot anticipate, such as emergent behaviors or adversarial attacks. Incident governance helps manage these failures through structured processes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What inconsistency does the paper identify in the current AI incident governance ecosystem?",{"text":80,"@type":76},"The paper finds lack of consistency across definitions, classification, monitoring, reporting, and incident analysis. These differences affect what data is collected and how it is interpreted.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the proposed principles aim to address the identified gaps?",{"text":84,"@type":76},"The paper proposes principles supported by concrete monitoring guidelines and a reporting template. 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