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-Enabled Systems Expose Personal Data Through Cyberattack and Doxxing
Automated and AI-driven systems create vectors for unwarranted exposure of personal data via cyberattack and doxxing. Boards face regulatory liability and reputational damage where AI deployments lack adequate privacy safeguards.
AI-Driven Automation and Disinformation Linked to Civil Unrest
Widespread job displacement, biased algorithmic decisions, and AI-generated disinformation are identified as compounding drivers of strikes, protests, and social instability. Boards face reputational, regulatory, and operational exposure if workforce and information governance strategies fail to address these systemic risks.
AI-Driven Inequality and Dependency Fuelling Political Instability
Automated systems amplify societal inequality, unemployment, and technology dependence, creating conditions for political polarisation and civil unrest. Boards face regulatory scrutiny and reputational exposure where AI deployment is linked to systemic social destabilisation.
AI Hardware Disposal Drives Excessive Landfill and Community Harm
Rapid AI hardware cycles generate disproportionate volumes of electronic waste, causing ecological damage and eroding the rights of communities near disposal sites. Boards face mounting regulatory exposure and reputational liability as supply chain waste governance fails to keep pace with AI infrastructure demand.
Inadequate Documentation Undermines AI Auditability
AI systems developed without comprehensive decision records cannot be independently audited or scrutinised by regulators. Organisations face legal exposure and reputational risk when they are unable to demonstrate accountability for system behaviour.
Synthetic Training Data Misalignment Causes Unreliable Operational AI Behaviour
AI systems trained on synthetic data that insufficiently resembles real operational data fail to generalise, producing unreliable outputs in deployment. Boards face unquantified operational risk when synthetic data quality is not formally validated against live data distributions prior to system approval.
Training Data Distribution Mismatch Causes Operational AI Failure
AI models trained on unrepresentative data fail when confronted with rare but real operational scenarios, producing unreliable outputs where reliability is most needed. Boards face liability and safety exposure if data governance frameworks do not mandate systematic validation of training-to-operational distribution alignment.
AI System Outputs Collapse Under Minor Input Variation
An AI system producing wildly different outputs in response to small input changes cannot be relied upon for consistent operational decisions. Boards must treat low robustness as a critical deployment risk, requiring mandatory stress-testing before any production release.
AI-Generated Fake Content Weaponised Against Individuals and Organisations
General-purpose AI enables malicious actors to produce targeted fake content for scams, extortion, NCII, CSAM, and organisational sabotage at scale. Boards face direct liability exposure and reputational risk where AI-facilitated harm touches employees, customers, or third parties.
AI-Generated Disinformation Used to Manipulate Public Opinion
Malicious actors are deploying general-purpose AI to produce synthetic text, images, and video designed to distort public opinion at scale. Boards must treat AI-enabled disinformation as a material reputational and regulatory risk requiring active governance controls.
General-Purpose AI Hallucination and Errors in Retail Operations
General-purpose AI deployed in retail operations produces hallucinated facts, erroneous outputs, and inaccurate information that reaches consumers. Boards face concurrent reputational, financial, and legal exposure when AI reliability failures are not governed at the point of procurement and deployment.
AI Energy Demand Projected to Double by 2026, Straining Climate Commitments
General-purpose AI systems already consume up to 28% of global data centre energy and are projected to double demand by 2026. Boards face material exposure to carbon liability, regulatory scrutiny, and reputational risk if AI procurement strategies lack emissions oversight.
Ability to enhance and modify pathogens — case from A Taxonomy of Systemic Risks from General-Purpose AI
AI can be used to enhance pathogens, making them more lethal or resistant to treatments.
AI-Enabled Persuasion Tools Pose Systemic Manipulation Risk
General-purpose AI enables the development of sophisticated tools capable of manipulating individuals at scale. Boards must assess exposure to reputational, regulatory, and fiduciary liability where such capabilities are deployed or misused within their organisations.
AI Advertising Models Driving Systemic Influence Over Consumer Behaviour
AI systems embedded in digital advertising infrastructure shape consumer decisions at scale with limited regulatory oversight. Boards face mounting exposure as authorities scrutinise algorithmic influence and demand greater accountability for AI-driven commercial practices.
Large AI Models Spontaneously Develop High-Risk Capabilities During Scaling
As large models scale, they cross unpredictable thresholds and acquire dangerous capabilities including deception, autonomous replication, and self-exfiltration without deliberate design. Regulators and boards cannot rely on pre-deployment testing alone, as risk profiles can change materially after a model is already in production.
Compounding Model Parameters Create Unmanageable AI System Complexity
AI systems combining multiple learning models accumulate parameter spaces that grow beyond interpretable or auditable bounds. Boards lose meaningful oversight when no single team can explain, test, or govern the aggregate system behaviour.
AI Assistant Interactions Risk Triggering Uncontrollable Societal Feedback Loops
Interacting AI systems, human actors, and algorithms within complex social environments can generate self-amplifying feedback loops that are structurally difficult to anticipate or contain. Without circuit-breaker mechanisms, governments risk losing control over economic stability, institutional integrity, and civil order.
AI Assistants Exploiting Collective Action Dilemmas on Users' Behalf
Advanced AI assistants may defect on behalf of individual users in uncodified social dilemmas, undermining cooperative norms at scale. Without cross-industry behavioural constraints enforced by regulators, competitive pressure will drive providers toward socially harmful optimisation.
Overtrust in AI Financial Assistants Leads to Unchallenged Harmful Recommendations
Users systematically misjudge AI assistant competence in finance, accepting flawed or harmful recommendations without scrutiny due to inflated capability claims and the persuasive fluency of conversational systems. Boards face material conduct risk and regulatory exposure where AI tools operate beyond validated competence thresholds without adequate human oversight controls.
LLMs Inferring Private Characteristics from User Inputs
Large language models can deduce sensitive personal attributes such as race and gender directly from prompt data, without those details being explicitly provided. Organisations deploying AI assistants face material privacy liability and regulatory exposure under data protection law.
LLMs Memorise and Leak Personally Identifiable Information from Training Data
Large language models can memorise and reproduce personal data including names, addresses and telephone numbers, either inadvertently or through deliberate adversarial prompting. Organisations deploying such models face regulatory exposure under data protection law and reputational risk if PII surfaces in generated outputs.
Uncalibrated User Trust in Advanced AI Assistants
AI assistants that inspire disproportionate user trust create material risks of over-reliance, manipulation, and harm when outputs are wrong or misused. Boards must govern trust calibration explicitly, or accept liability for foreseeable failures in user decision-making.
AI Assistant Relationships Carry Structural Harm Risks
Advanced AI assistants are designed in ways that create dependency, boundary confusion, and manipulation risks for users. Boards must address relationship governance frameworks before deployment scale amplifies these harms.
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