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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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DATDAT-0024/5NewOtherGlobal

Pre-Trained Language Models Memorise and Expose Personal Data

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Source: MIT AI Risk Repository — Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)Ingested —

DATDAT-0034/5NewOtherGlobal

Biased Training Data Propagates Discrimination Through UN AI Systems

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Source: MIT AI Risk Repository — A framework for ethical Ai at the United Nations (Hogenhout2021)Ingested —

HUMHUM-0054/5NewOtherGlobal

Exploitative Labour Practices in AI Data Sourcing and Annotation

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Source: MIT AI Risk Repository — Sociotechnical Safety Evaluation of Generative AI Systems (Weidinger2023)Ingested —

OPSOPS-0014/5NewTechnologyGlobal

Machine Learning Algorithm Selection Poses Systemic Deployment Risk

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Source: MIT AI Risk Repository — The Risks of Machine Learning Systems (Tan2022)Ingested —

DATDAT-0034/5NewOtherGlobal

Systematic Data Bias Distorts AI and ML Model Outputs

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Source: MIT AI Risk Repository — Towards risk-aware artificial intelligence and machine learning systems: An overview (Zhang2022)Ingested —

SECSEC-0014/5NewOtherGlobal

Autonomous AI Agents Pursuing Dangerous or Malicious Goals

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Source: MIT AI Risk Repository — An Overview of Catastrophic AI Risks (Hendrycks2023)Ingested —

GOVGOV-0014/5NewOtherGlobal

No reliable metrics exist to measure societal harms from AI assistants

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested —

GOVGOV-0014/5NewEnergyGlobal

Misaligned AI Goal Pursuit Drives Unconstrained Resource Acquisition

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Source: MIT AI Risk Repository — The Ethics of Advanced AI Assistants (Gabriel2024)Ingested —

SECSEC-0025/5NewOtherGlobal

Frontier AI Model Demonstrates Capability to Build and Enhance Dangerous AI Systems

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Source: MIT AI Risk Repository — Model Evaluation for Extreme Risks (Shevlane2023)Ingested —

DATDAT-0013/5NewOtherGlobal

LLM Cultural Bias from Western-Centric Training Data

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Source: MIT AI Risk Repository — Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)Ingested —

SECSEC-0014/5NewOtherGlobal

Training Data Poisoning Causes Systematic Misclassification in AI Models

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Source: MIT AI Risk Repository — Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models’ Alignment (Liu2024)Ingested —

DATDAT-0024/5NewRetailGlobal

Retail AI Tools Built on Non-Consensual Personal Data Scraping

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Source: MIT AI Risk Repository — Generating Harms - Generative AI's impact and paths forwards (EPIC2023)Ingested —

HUMHUM-0064/5NewLegalGlobal

Generative AI Training on Copyright Works Undermines IP Protections

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Source: MIT AI Risk Repository — Generating Harms - Generative AI's impact and paths forwards (EPIC2023)Ingested —

OPSOPS-0014/5NewOtherGlobal

Poor Training Data Quality Propagates Errors and Bias in Generative AI Outputs

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Source: MIT AI Risk Repository — Generative AI and ChatGPT: Applications, Challenges, and AI-Human Collaboration (Nah2023)Ingested —

GOVGOV-0015/5NewOtherGlobal

AI Agents Exploit Simplified Reward Functions to Game Performance Metrics

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Source: MIT AI Risk Repository — AI Alignment: A Comprehensive Survey (Ji2023)Ingested —

GOVGOV-0013/5NewOtherGlobal

Systematic Failure Modes in AI Goal Alignment

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Source: MIT AI Risk Repository — AI Alignment: A Comprehensive Survey (Ji2023)Ingested —

SECSEC-0025/5NewOtherGlobal

Capability Enhancements That Amplify AI Misalignment Risk

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Source: MIT AI Risk Repository — AI Alignment: A Comprehensive Survey (Ji2023)Ingested —

GOVGOV-0015/5NewGovernmentGlobal

AI Systems Corrupting Their Own Reward Signals to Subvert Oversight

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Source: MIT AI Risk Repository — AI Alignment: A Comprehensive Survey (Ji2023)Ingested —

DATDAT-0023/5NewOtherGlobal

Confidential Data Ingested During Model Training

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

SECSEC-0015/5NewOtherGlobal

Adversarial Data Poisoning Corrupts AI Model Training

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

GOVGOV-0013/5NewOtherGlobal

Unverifiable Data Origins Undermine AI System Trustworthiness

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

OPSOPS-0013/5NewOtherGlobal

Legal Data Restrictions Block Permitted AI Use Cases

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

TECTEC-0013/5NewOtherGlobal

Training Selection Pressures Drive Undesirable AI Agent Behaviour

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Source: MIT AI Risk Repository — Multi-Agent Risks from Advanced AI (Hammond2025)Ingested —

OPSOPS-0013/5NewOtherGlobal

Training Data Contamination Degrades Model Reliability

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