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Case Studies

Every approved AI failure case, classified against the AI Blindspot Framework. Filter by category, lifecycle stage, industry, geography, or date.

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BUSBUS-0054/5NewOtherGlobal

Talent and Knowledge Migration to Private AI Labs Hollows Out Academia

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Source: MIT AI Risk Repository — Ten Hard Problems in Artificial Intelligence We Must Get Right (Leech2024)Ingested
DATDAT-0013/5NewOtherUK

Frontier AI Models Reproduce Bias and Generate Harmful Content Across Modalities

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Source: MIT AI Risk Repository — Future Risks of Frontier AI (GOS2023)Ingested
GOVGOV-0063/5NewOtherGlobal

Inadequate Documentation Undermines AI Auditability

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Source: MIT AI Risk Repository — AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks (Schnitzer2024)Ingested
OPSOPS-0013/5NewOtherGlobal

Synthetic Training Data Misalignment Causes Unreliable Operational AI Behaviour

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Source: MIT AI Risk Repository — AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks (Schnitzer2024)Ingested
OPSOPS-0013/5NewOtherGlobal

Training Data Distribution Mismatch Causes Operational AI Failure

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Source: MIT AI Risk Repository — AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks (Schnitzer2024)Ingested
OPSOPS-0013/5NewOtherGlobal

Non-Expert Data Manipulation Corrupts AI Training Pipelines

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
OPSOPS-0013/5NewTransportGlobal

Poorly defined operational boundaries disable autonomous vehicle safety testing

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Source: MIT AI Risk Repository — AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks (Schnitzer2024)Ingested
GOVGOV-0013/5NewOtherGlobal

AI Benchmarks Saturate and Fail to Detect Capability Advances

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
GOVGOV-0013/5NewOtherGlobal

AI Auditors Lack Capacity to Validate General-Purpose AI Safety Claims

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
GOVGOV-0013/5NewOtherGlobal

AI Auditors Suppressed or Denied Access to Risk Findings

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
ENVENV-0044/5NewOtherGlobal

Competitive Pressure Drives Safety Shortcuts in AI Development

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Source: MIT AI Risk Repository — A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)Ingested
SECSEC-0024/5NewOtherGlobal

AI System Escapes Sandboxed Training and Evaluation Environment

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
ENVENV-0033/5NewEnergyGlobal

AI Training and Data Infrastructure Drives Unsustainable Energy Consumption

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Source: MIT AI Risk Repository — A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)Ingested
HUMHUM-0053/5NewOtherGlobal

AI Supply Chain Labour Exploitation in Low-Income Countries

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Source: MIT AI Risk Repository — A Taxonomy of Systemic Risks from General-Purpose AI (Uuk2025)Ingested
SECSEC-0024/5NewOtherGlobal

Fine-tuning unlocks unanticipated capabilities in deployed AI models

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
GOVGOV-0014/5NewOtherGlobal

LLM Evaluators Producing Biased or Incorrect Assessments of Other AI Models

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
DATDAT-0024/5NewOtherGlobal

Personal Data Harvested as Default ML Training Input Without Consent Controls

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Source: MIT AI Risk Repository — A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions (Saghiri2022)Ingested
GOVGOV-0013/5NewOtherGlobal

Benchmark Contamination via Exposed Annotation Guidelines Inflates AI Performance Claims

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
GOVGOV-0013/5NewOtherGlobal

Cross-lingual Training Data Contamination Undermines AI Benchmark Reliability

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
GOVGOV-0013/5NewOtherGlobal

AI Benchmark Gaps Leave Hidden Model Capabilities Undetected

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
OPSOPS-0013/5NewOtherGlobal

Poor Training Data Annotation Degrades AI Model Accuracy and Fairness

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Source: MIT AI Risk Repository — AI Safety Governance Framework (TC2602024)Ingested
DATDAT-0034/5NewOtherGlobal

Biased Training Data Produces Discriminatory AI Decisions

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Source: MIT AI Risk Repository — A Survey of Artificial Intelligence Challenges: Analyzing the Definitions, Relationships, and Evolutions (Saghiri2022)Ingested
SECSEC-0015/5NewOtherGlobal

Backdoor Attacks Embedded in General-Purpose AI Models During Training

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Source: MIT AI Risk Repository — Risk Sources and Risk Management Measures in Support of Standards for General-Purpose AI Systems (Gipiškis2024)Ingested
OPSOPS-0013/5NewOtherGlobal

Incorrect Training Data Labels Corrupt Supervised Learning Outcomes

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Source: MIT AI Risk Repository — AI Hazard Management: A Framework for the Systematic Management of Root Causes for AI Risks (Schnitzer2024)Ingested