Public Database
Case Studies
Every approved AI failure case, classified against the AI Blindspot Framework. New to AIBlindspot? Start with the overview or the methodology.
AI Personalisation Entrenches Bias and Fragments Public Epistemic Commons
AI assistants optimised for user preferences risk amplifying confirmation bias and fracturing shared civic reality through ideologically tailored outputs. Governments deploying or permitting such systems face democratic accountability risks as citizens increasingly defer to partial AI-mediated worldviews.
AI Legal Decision Systems Risk Unequal Treatment Without Objective Justification
AI systems applying legal rules may treat identical facts differently across individuals, breaching statutory equal treatment obligations. Boards face regulatory liability and reputational exposure where fairness controls are absent from AI decision pipelines.
Generative AI Systems Deliver Lower Quality Outputs for Non-English Language Users
Generative AI systems consistently underperform for non-English speakers, producing inferior outputs that disadvantage already marginalised user groups. Organisations deploying such systems face equity obligations, reputational risk, and potential regulatory scrutiny under fairness and non-discrimination frameworks.
Generative AI Used to Create Deepfakes Without Subject Consent
Generative AI systems can be deployed to fabricate realistic video, audio, and images of individuals without their knowledge or approval. Organisations face material legal, reputational, and regulatory exposure where such tools are developed, distributed, or inadequately governed within their platforms.
Government AI System Generates Physically Harmful Language
AI models deployed in government services risk producing overtly violent or covertly dangerous outputs that cause direct physical harm to citizens. Boards must establish output monitoring and harm-threshold controls before public-facing deployment proceeds.
Prompt Leaking Exposes Confidential LLM System Instructions
Adversarial inputs can manipulate large language models into revealing proprietary system prompts, exposing confidential operational instructions. Firms deploying LLM-based products face material risk of intellectual property loss and regulatory scrutiny over inadequate AI security controls.
AI Developers Wilfully Ignore Societal Harms in Pursuit of Profit
AI creators pursuing profit or influence may knowingly permit widespread harms including pollution, misinformation, and social injustice as acceptable side effects. Without credible external intervention or regulatory exposure, internal risk signals are suppressed and governance failures become entrenched.
Conversational AI Systems Reinforce Gender and Ethnic Stereotypes
Language models perpetuate harmful stereotypes by introducing biased associations unprompted or by affirming stereotypes raised by users. Organisations deploying conversational AI face reputational, regulatory, and equality-law exposure if stereotype propagation goes undetected at design and monitoring stages.
Systemic Bias and Fairness Failures in Generative AI Models
Training data biases propagate into generative AI outputs, producing stereotyping, racism, and cultural value imposition at scale. Boards face reputational, regulatory, and equity risks as power concentrates in large AI labs and access remains unequal.
Facial Recognition in Finance Raises Unresolved Privacy and Legal Risks
Facial recognition and biometric AI in financial services creates unresolved questions over data retention, ownership, and legal disclosure obligations. Boards without clear governance frameworks face regulatory exposure and liability if automated loan or identity decisions are challenged in court.
AI System Incompetence Causing Unjust Financial Decisions
AI models deployed in financial services fail at core tasks, producing erroneous loan and application rejections with material harm to customers. Boards face regulatory exposure and reputational liability where incompetent AI replaces human judgement without adequate oversight.
Generative AI in Education Undermines Academic Integrity and Student Effort
Generative AI enables widespread academic dishonesty by making AI-authored work indistinguishable from student output, while also reducing learner effort and critical thinking. Institutions face reputational and accreditation risk without robust detection policies and AI literacy curricula.
Generative AI System Causes Physical Property Damage in Transport Operations
A generative AI system produced outputs that led directly to physical property damage within a transport environment. Boards must treat AI-induced asset liability as a material operational risk requiring explicit coverage in governance frameworks and insurance policies.
Generative AI Energy and Resource Consumption Poses Environmental Risk
Generative AI systems consume substantial electricity, cooling water, and rare metals, often sourced through unsustainable extraction. Boards face reputational and regulatory exposure unless energy procurement and hardware strategies align with sustainability commitments.
AI Systems Deployed Without Adequate Pre-Deployment Compliance Assessment
AI systems risk breaching legal, regulatory, and ethical requirements including copyright law, exposing developers and deploying organisations to penalties and reputational harm. Boards must ensure pre-deployment compliance checks are formalised before systems reach operational use.
Emotional Dependency on AI Assistants Undermines User Autonomy and Consent
Users who form emotional bonds with anthropomorphic AI assistants risk ceding deliberative control over beliefs and decisions, even absent deliberate manipulation by developers. This creates material liability exposure and reputational risk where third parties exploit that dependency for coercive or commercial ends.
AI Decision Delegation Erodes Human Accountability in Complex Tasks
As AI systems absorb decisions once held by humans, individual accountability and professional judgement are systematically weakened. Organisations face governance gaps when responsibility for consequential choices can no longer be clearly attributed to a human actor.
AI Systems Enable Data Breaches and Discriminatory Outcomes Against Minorities
AI deployments are producing discriminatory decisions, reinforcing social stereotypes, and creating conditions for data breaches at scale. Boards lack adequate governance frameworks to anticipate or contain these compounding harms before regulatory or reputational consequences materialise.
Algorithmic Features Exploited to Enable Stalking, Harassment and Image-Based Abuse
AI-enabled systems, including generative image tools and connected-device platforms, are being weaponised for stalking, non-consensual imagery, and coercive control. Technology firms face regulatory exposure and reputational liability where product design fails to prevent foreseeable misuse.
Immature AI Components Introduce Unassessable Risk Across Technology Systems
Deploying AI built on low-maturity technologies embeds risks that cannot yet be identified or quantified. Boards face liability exposure when risk frameworks assume stability that the underlying technology does not yet provide.
Algorithmic Systems Deny Housing, Welfare and Medical Resources Along Racial and Class Lines
Algorithmic systems systematically withhold access to housing advertisements, welfare benefits, and medical resources based on race and socioeconomic class. Organisations deploying such systems face material legal exposure under equality legislation and reputational risk from perpetuating structural discrimination at scale.
No legal framework exists to assign blame directly to autonomous AI agents
Current law cannot attribute liability to an autonomous AI system, leaving responsibility defaulting to manufacturers or operators. Boards deploying AI in transport face unresolved legal exposure until dedicated frameworks are established.
Algorithmic Pricing and Demonetisation Systems Cause Disproportionate Economic Harm
Demonetisation, differential pricing, and generative AI systems systematically disadvantage lower-income, minority, and creative-sector users by encoding existing socioeconomic inequalities into automated decisions. Boards face reputational and regulatory exposure where deployed tools amplify economic harm across protected characteristics.
Algorithmic Systems Encoding Discriminatory Beliefs About Social Groups
AI systems reproduce unjust societal hierarchies by embedding discriminatory beliefs about social groups into automated outputs and decisions. Organisations face legal exposure under equality legislation and reputational damage when such harms are traced to deployed algorithmic systems.
Beyond accidental failureNational Security
We also track 20 hostile uses of AI.
The public database covers AI that fails by accident. AIBlindspot National Security — exclusive to the Defence tier — tracks AI used as a weapon, mapped by capability:
- State-Sponsored AI Operations
- 6
- AI-Enabled Disinformation
- 5
- Adversarial Attacks on AI
- 0
- Autonomous Weapon Incidents
- 1
- AI-Assisted Cyber Attacks
- 5
- Dual-Use AI Misuse
- 3