> ## Content Index > Fetch the complete content index at: https://www.aigl.blog/llms.txt > Use this file to discover other available public pages before exploring further. # AI Incidents: Key Components for a Mandatory Reporting Regime - URL: https://www.aigl.blog/ai-incidents-key-components-for-a-mandatory-reporting-regime/ - Published: 2026-09-19T09:07:21.000Z - Updated: 2026-09-19T09:07:21.000Z - Description: A CSET issue brief proposing a standardized list of key components for AI incidents β€” event type, harm dimensions, technical data, context and post-incident data β€” as a template for mandatory AI incident reporting regimes. - Author: Jakub Szarmach - Tags: Briefing, AI Governance, AI Safety, Risk Management, AI Policy & Regulation, Compliance & Audit, #aigl-library ## ⚑ Quick Summary Published by the Center for Security and Emerging Technology (CSET), this issue brief sets out the content a mandatory AI incident reporting regime should collect. It follows the authors' earlier paper, "An Argument for Hybrid AI Incident Reporting," which proposed a federated framework combining mandatory, voluntary and citizen reporting to an independent external entity; this document addresses the mandatory strand and is offered as a guide for voluntary and citizen schemes too. The brief adopts OECD definitions of AI incident, near miss and hazard, argues that near misses belong in mandatory reporting while hazards do not, and synthesizes its components from twelve AI initiatives and from transportation, healthcare and cybersecurity reporting systems. Its key components are type of event; type of harm (physical, environmental, economic and financial, reputational, public interest, human rights and fundamental rights, psychological); mechanism of harm (technical and other factors); severity factors; technical information through AI system cards, model cards and datasheets; context, circumstances and stakeholders; and post-incident data. It closes with two recommendations: publish AI incident reporting formats based on the components, and establish an independent investigation agency. ## 🧩 What’s Covered - **Framing and definitions:** adopts OECD definitions of AI incident, AI near miss and AI hazard, and argues that near misses belong in mandatory reporting while hazards, described as unsafe conditions, generally do not. - **Method and selection criteria:** components were synthesized from twelve AI incident databases and documents and from reporting systems in transportation, healthcare and cybersecurity; the selection criteria were ease of use, adaptability and functionality. - **Type of event:** the first reporting step is deciding whether an event is an incident or a near miss, illustrated by a deepfake that briefly moved the stock market and an autonomous vehicle that failed to stop at a stop sign. - **Harm dimensions:** types of harm; mechanism of harm, split into technical factors such as malfunctions, system vulnerabilities, data poisoning and concept drift, and other factors such as weak governance, user misuse and user abuse; and seven severity factors including remediability, exposed population size, duration, optionality and frequency. - **Technical data:** mandatory reporters would submit AI system cards, AI model cards and datasheets covering data, models, code, intended use, evaluation data and training data. - **Context, circumstances and stakeholders:** goals and application purpose, sector, start and end date, location, reporter, existing safeguards and policies, AI actors, and affected stakeholders, including whether they were users or nonusers of the system. - **Post-incident data:** incident response such as mitigation and termination, plus ethical impact assessed with the UNESCO ethical impact assessment tool or the OECD AI Principles. - **Appendix analysis:** reviews the twelve AI initiatives and the sectoral reporting systems, treats AI harm as a spectrum, notes that technical and severity data are thinly documented in current databases, and describes severity measurement including the CVSS calculator's 0–10 range. ## πŸ’‘ Why it matters? For regulators, oversight bodies and developers who must record and monitor AI harms, the brief supplies a shared vocabulary and a component list that can be turned directly into reporting templates, reducing the inconsistency that makes incident data hard to aggregate and compare. It explains why near misses and pre-existing safeguards deserve a place in reporting, and where technical evidence such as model cards and datasheets is currently absent. The appendix connects the proposal to the EU AI Act, the Council of Europe Framework Convention on AI and the NIST AI Risk Management Framework as regimes that mandate or recommend impact assessment. ## ❓ What’s Missing Severity assessment is explicitly deferred: developing an AI incident severity rating or metric framework, the authors state, requires further exploration and is not discussed here. The component list is deliberately high level and not exhaustive, with granularity and response formats left to regulators, and no template, reporting deadline, threshold or enforcement mechanism is specified. The relationship between mandatory AI incident reporting and existing sectoral regimes is not worked out. The synthesis rests on the authors' review of twelve AI initiatives and three sectors, and the brief notes that technical and severity data are poorly documented in current incident databases. ## πŸ‘₯ Best For Best for policy analysts and regulators designing or reviewing AI incident reporting requirements, oversight committees drafting reporting formats, and incident database maintainers seeking a common component set. Also useful for AI developers and safety teams that need to know what incident data mandatory reporting would demand, and for researchers comparing incident taxonomies across the AI, transportation, healthcare and cybersecurity sectors. ## πŸ“„ Source Details *AI Incidents: Key Components for a Mandatory Reporting Regime*, an issue brief from the Center for Security and Emerging Technology by Ren Bin Lee Dixon and Heather Frase, dated January 2025\. 32 pages, in English. Document Identifier: doi: 10.51593/20240023\. The extraction covered all 32 pages, including the executive summary, tables 1–6, the two appendix tables, endnotes and author biographies. No URL for the brief itself is printed; endnotes cite external sources.