Public Database
Case Studies
Every approved AI failure case, classified against the AI Blindspot Framework. Filter by category, lifecycle stage, industry, geography, or date.
Algorithmic Bias and Opacity Identified as Dominant AI Ethics Failures
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AI Systems Manipulating Their Own Training Signals to Subvert Intended Goals
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AI Systems Using Dark Patterns and Covert Nudging to Manipulate User Behaviour
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Homogeneous AI Models Drive Synchronised Market Instability in Finance
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AI Systems Cause Discriminatory Outcomes Against Protected Groups
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Demographic Homogeneity in AI Research and Development Workforce
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Western bias and unequal participation in AI ethics frameworks
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AI Capabilities Enabling Surveillance and Democratic Process Manipulation
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AI Market Concentration Threatens Competition and Access
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Ethical Risks in AI Systems Designed to Adapt to Human Behaviour at Work
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AI-Driven Automation Risks Structural Unemployment Among Low- and Middle-Income Workers
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Dominant AI Models Creating Systemic Monoculture Risk
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AI Systems Raise Systemic Risks of Personal Data Exploitation
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Flawed Model Design Choices Produce Biased and Unreliable AI Systems
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AI Computing Infrastructure Exposed to Resource Hijacking and Cross-Boundary Security Threats
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AI-Generated Content Undermines User Authenticity and Identity Verification
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AI-Driven Human Role Substitution Creates Systemic Societal Risk
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Generative AI Technical Vulnerabilities in Transport Operations
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Training Data Contamination Undermines AI Benchmark Reliability
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AI Systems Delivering Unqualified Specialist Advice Across Finance, Health, Law and Elections
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Diffuse AI Accountability Across Multi-Party Development Chains
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Generative AI Systems Present False Information as Authoritative Fact
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AI-Enabled Cognitive Warfare and Disinformation Infrastructure
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General Purpose AI Lowers Barriers to Biological Weapons Development
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