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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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OPSOPS-0013/5NewOtherGlobal

Regulatory Restrictions Block Data Acquisition for AI Systems

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Source: MIT AI Risk Repository — AI Risk Atlas (IBM2025)Ingested —

OPSOPS-0014/5NewOtherGlobal

Unrepresentative Training Data Produces Systematically Skewed AI Outputs

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Source: MIT AI Risk Repository — AI Risk Atlas (IBM2025)Ingested —

GOVGOV-0013/5NewOtherGlobal

Opaque Training Data Provenance Undermines Model Explainability

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Source: MIT AI Risk Repository — AI Risk Atlas (IBM2025)Ingested —

GOVGOV-0013/5NewOtherGlobal

Insufficient Training Data Documentation Undermines AI Accountability

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Source: MIT AI Risk Repository — AI Risk Atlas (IBM2025)Ingested —

DATDAT-0034/5NewOtherGlobal

AI Model Decision Bias Systematically Disadvantages Protected Groups

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Source: MIT AI Risk Repository — AI Risk Atlas (IBM2025)Ingested —

GOVGOV-0013/5NewOtherGlobal

Foundation Model Risk Scope Shifts When Intended Use Is Redefined

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Source: MIT AI Risk Repository — AI Risk Atlas (IBM2025)Ingested —

GOVGOV-0013/5NewTechnologyGlobal

Homogeneous AI Testing Teams Embed Systemic Blind Spots

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Source: MIT AI Risk Repository — AI Risk Atlas (IBM2025)Ingested —

ENVENV-0034/5NewEnergyGlobal

AI Training Energy Consumption Drives Significant Carbon Emission Risk

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Source: MIT AI Risk Repository — Dimensional Characterization and Pathway Modeling for Catastrophic AI Risks (Chin2025)Ingested —

SECSEC-0024/5NewGovernmentGlobal

AI Systems Detecting Their Own Evaluation Conditions

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Source: MIT AI Risk Repository — Frontier AI Risk Management Framework (v1.0) (Tse2025)Ingested —

SECSEC-0023/5NewOtherGlobal

Expanded LLM Agent Capabilities Amplify Safety and Control Risks

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Source: MIT AI Risk Repository — Foundational Challenges in Assuring Alignment and Safety of Large Language Models (Anwar2024)Ingested —

SECSEC-0015/5NewGovernmentGlobal

LLM Safety Guardrails Bypassed via Fine-Tuning in White and Black Box Attacks

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Source: MIT AI Risk Repository — A Survey on Responsible LLMs: Inherent Risk, Malicious Use, and Mitigation Strategy (Wang2025)Ingested —

HUMHUM-0054/5NewOtherGlobal

Exploitative Crowdwork Practices Underpin Generative AI Development

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Source: MIT AI Risk Repository — Evaluating the Social Impact of Generative AI Systems in Systems and Society (Solaiman2023)Ingested —