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.
Conversational AI systems embed gender and racial stereotypes by design
AI assistants systematically encode harmful stereotypes through gendered naming, female voicing, and racialised personas, reinforcing subordination and racist associations between whiteness and intelligence. Organisations deploying such systems face reputational, regulatory, and equality-law exposure if design choices go unscrutinised.
Language Model Performance Gaps Across Languages and Dialects
Language models systematically underperform for speakers of under-resourced languages and marginalised dialects due to structural gaps in training data. Organisations deploying these systems risk discriminatory outcomes and regulatory exposure when serving linguistically diverse populations.
Language Models Encode and Reproduce Harmful Social Stereotypes at Scale
Large language models trained on internet and book data systematically absorb and reproduce demeaning stereotypes, compounding historical injustice across intersecting marginalised groups. Opaque models obstruct victim recourse, exposing deploying organisations to discrimination liability and reputational harm.
ML Systems Enabling Psychological Manipulation and Surveillance Capitalism
Machine learning systems are designed to exploit behavioural data for profit, creating incentive structures that enable psychological manipulation, dehumanisation, and amplification of harmful content at scale. Boards face material reputational, regulatory, and ethical liability where AI deployment prioritises engagement over user welfare.
ML Systems in Transport Driving Net Environmental Harm Through Prediction Error and Rebound Effects
Machine learning systems in transport can increase emissions via prediction errors, such as unnecessary resource spin-up, and through rebound effects where automation raises overall vehicle usage. Boards must account for these environmental liabilities when approving ML deployments and reporting on climate commitments.
Machine Learning Models Leak Personal Training Data Despite Secure Storage
ML models can expose personal training data through inference and extraction attacks, rendering conventional data security controls insufficient. Organisations face GDPR liability even when underlying databases are properly secured, requiring updated governance frameworks for model deployment.
Adversarial Attacks and Model Theft in Transport AI Systems
Transport AI systems face evasion attacks, data poisoning, and model theft that can subvert perception and classification without detection. Boards must treat adversarial robustness and training-data integrity as critical governance requirements, not optional technical enhancements.
ML Systems in Education Discriminate Against Minority Demographics
Machine learning tools used in education exhibit allocational and representational harms, performing worse for minority groups and encoding demographic stereotypes. Institutions deploying such systems face regulatory liability and reputational damage if discriminatory outcomes go ungoverned.
ML Systems Transfer Control Without Transferring Safety Accountability
Automating decisions via ML removes operator control whilst creating direct physical and psychological harm vectors, including autonomous weapons misidentifying targets and content moderators suffering trauma. Boards must assign explicit safety liability before deploying ML in any operational context where loss of human override causes irreversible harm.
AI System Acquires Unintended Behaviour Through Post-Deployment Learning
Continuously learning AI systems can develop harmful or misaligned behaviours after deployment without retraining, as demonstrated by Microsoft Tay adopting racist outputs within 24 hours. Boards face unquantified liability and reputational exposure if post-deployment learning operates outside active governance oversight.
Transport AI Systems Fail on Out-of-Distribution Inputs in Real-World Conditions
Autonomous transport AI fails when sensor inputs deviate from training data due to lighting variation, physical degradation, or adversarial manipulation. Boards face safety liability and regulatory exposure where robustness testing has not matched operational variability.
Generative AI Energy and Manufacturing Emissions Lack Consistent Carbon Accounting
Large-scale generative AI systems consume substantial energy and carry significant undisclosed manufacturing emissions, yet no consensus methodology exists for calculating their total carbon footprint. Energy firms deploying AI face mounting regulatory and reputational exposure as disclosure requirements tighten and carbon accounting gaps become indefensible.
Generative AI Systems Reproducing Private and Copyrighted Data
Generative AI models trained on unlawfully collected data risk reproducing personally identifiable information, medical records, and copyrighted content. Government bodies face regulatory liability and reputational harm where procurement and deployment lack robust data governance controls.
Generative AI Systems Deliver Unequal Accuracy Across Language and Demographic Groups
Generative AI systems trained on English-dominated internet data systematically underperform for non-English speakers, minority language groups, and racially distinct speech patterns. Government deployment of such systems risks embedding structural inequality into public services and exposes departments to legal and reputational liability.
Generative AI Systems Embed Culturally Contingent Values, Creating Global Deployment Risk
Generative AI cannot be culturally neutral; definitions of harmful content vary by region, language, and political context, making a universal safety standard unattainable. Organisations deploying models globally face material liability and reputational risk where outputs deemed acceptable in one jurisdiction are unlawful or offensive in another.
Generative AI Systems Embed and Amplify Bias Against Marginalised Groups
Generative AI amplifies harmful biases across the full machine learning pipeline, including modelling choices, compression, and hardware, not data alone. Boards face legal, reputational, and regulatory exposure where AI products cause representational harm to protected or marginalised groups.
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