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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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GOVGOV-0063/5NewOtherGlobal

Over-Transparency in AI Systems Enables Misuse by End Users

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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-0014/5NewOtherGlobal

Proxy misspecification in goal-directed AI systems

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Source: MIT AI Risk Repository — X-Risk Analysis for AI Research (Hendrycks2022)Ingested —

GOVGOV-0013/5NewOtherGlobal

AI Evaluation Frameworks Systematically Underweight Hard-to-Measure Human Values

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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-0034/5NewOtherGlobal

Algorithmic Bias and Opacity Identified as Dominant AI Ethics Failures

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Source: MIT AI Risk Repository — What Ethics Can Say on Artificial Intelligence: Insights from a Systematic Literature Review (Giarmoleo2024)Ingested —

OPSOPS-0013/5NewOtherGlobal

Poor Model Design Choices from Unreviewed Developer Decisions

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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 —

SECSEC-0015/5NewOtherGlobal

Fine-tuning dataset poisoning enables covert manipulation of AI model behaviour

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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 —

SECSEC-0013/5NewOtherGlobal

Poor Data Quality Controls Undermine AI Performance and 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 —

OPSOPS-0013/5NewOtherGlobal

Poor Cross-Organisational Data Documentation Corrupts Shared AI Training Sets

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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

AI Benchmarks Systematically Misrepresent Model Capabilities

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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-0015/5NewOtherGlobal

AGI Systems Risk Fatal Errors During Active Learning Phases

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Source: MIT AI Risk Repository — AGI Safety Literature Review (Everitt2018)Ingested —

OPSOPS-0013/5NewOtherGlobal

Simulated agents manipulating AI decision distributions

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Source: MIT AI Risk Repository — AGI Safety Literature Review (Everitt2018)Ingested —

ENVENV-0034/5NewEnergyGlobal

Deep Learning Systems Drive Unsustainable Energy Consumption in Energy Sector

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

GOVGOV-0063/5NewOtherGlobal

Opaque AI Supply Chain Components Undermine Downstream Accountability

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Source: MIT AI Risk Repository — Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST2024)Ingested —

GOVGOV-0015/5NewOtherGlobal

AI Systems Manipulating Their Own Training Signals to Subvert Intended Goals

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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

Flawed Model Design Choices Produce Biased and Unreliable AI Systems

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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-0014/5NewTechnologyGlobal

Model Overfitting Degrades Operational AI Reliability

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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

Benchmark Annotation Contamination Invalidates AI Capability Evaluations

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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

Safety Benchmarks Lag Behind Performance Metrics in AI Evaluation

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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 —

SECSEC-0014/5NewGovernmentGlobal

Deliberate Pre-Deployment Sabotage of AI Systems by Insiders or Hackers

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Source: MIT AI Risk Repository — Taxonomy of Pathways to Dangerous Artificial Intelligence (Yampolskiy2016)Ingested —

GOVGOV-0013/5NewOtherGlobal

Pre-Deployment Design Errors Producing Misaligned AI Behaviour

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Source: MIT AI Risk Repository — Taxonomy of Pathways to Dangerous Artificial Intelligence (Yampolskiy2016)Ingested —

OPSOPS-0013/5NewOtherGlobal

Adversarial Training Produces Models That Become Less Robust Over Time

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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-0043/5NewOtherUK

AI Development Triggers Resource Conflicts Over Data Centres and Semiconductors

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Source: MIT AI Risk Repository — A Survey of the Potential Long-term Impacts of AI: How AI Could Lead to Long-term Changes in Science, Cooperation, Power, Epistemics and Values (Clarke2023)Ingested —

GOVGOV-0063/5NewOtherGlobal

AI Decision Intelligibility Gap Undermines Human Oversight

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Source: MIT AI Risk Repository — AGI Safety Literature Review (Everitt2018)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 —