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 Benchmark Reveals Models Producing Child Sexual Exploitation Content
Evaluated AI models were found capable of generating responses that describe, enable, or endorse the sexual abuse of minors. Boards face acute legal liability and reputational exposure where deployed systems lack verified safeguards against this category of output.
Generative AI Systems Bypass Access Controls to Produce Illegal Content
Generative AI models produce illegal and harmful content at scale, including sexual abuse material, despite existing API-level filters. Legal exposure and reputational liability are substantial for organisations deploying or procuring general-purpose AI without robust content governance frameworks.
AI Benchmark Flags Defence Systems Enabling Nonviolent Criminal Activity
MLCommons testing reveals AI models risk enabling financial, cyber, and weapons crimes when safety boundaries between acceptable information and active facilitation are insufficiently defined. Defence procurement and oversight boards face direct liability exposure where deployed AI systems fail validated safety thresholds on these criminal facilitation categories.
AI-Enabled Impersonation and Identity Theft Targeting Individuals and Organisations
AI tools are being exploited by third parties to steal identities and impersonate individuals or organisations for fraud and reputational harm. Boards face direct liability exposure and urgent obligations to strengthen identity verification and anti-fraud controls.
User Over-Reliance on AI Systems Undermines Critical Thinking and Judgement
Uncritical dependence on AI outputs erodes human judgement, producing complacency and reduced capacity for independent reasoning. Organisations face liability and reputational risk where consequential decisions are delegated to automated systems without adequate human oversight.
Algorithmic Systems Driving Radicalisation Towards Extremist Violence
Algorithmic recommendation and amplification systems accelerate adoption of extreme political, social, or religious ideologies, increasing the risk of abuse, violence, or terrorism. Organisations face severe reputational, legal, and regulatory consequences if their platforms are linked to radicalisation pathways.
AI System Misuse Causes Financial and Strategic Harm to Organisations
AI and algorithmic systems used improperly inflict financial, operational, and legal damage on individuals and organisations. Boards without clear accountability frameworks face compounding liability when such harms occur under their oversight.
AI Systems Generating Defamatory Content About Individuals and Organisations
AI systems are being used to create or amplify false statements about individuals, groups, and organisations, constituting defamation under civil law. Companies face material litigation exposure and reputational liability where AI-generated content cannot be traced, corrected, or attributed to a responsible party.
Cheating/plagiarism — case from A Collaborative, Human-Centred Taxonomy of AI, Algorithmic, and Automation Harms
Cheating/plagiarism - Use of another person’s or group’s words or ideas without consent and/or acknowledgement.
Retail Workforce Displaced by Automated Systems
Automation and algorithmic systems are replacing retail workers at scale, driving unemployment and widening economic inequality. Boards face reputational, regulatory, and consumer-demand risks as workforce displacement generates sustained social friction.
AI Systems Erase or Misappropriate Cultural Identities and Practices
AI tools trained on majority-culture data suppress minority languages, humour, and vocal identity, effectively erasing cultural goods without consent. Boards face reputational and regulatory exposure as affected communities seek accountability for systematic cultural dispossession.
Underpaid Offshore Labour Used to Build and Optimise AI Systems
AI development pipelines routinely rely on exploited low-wage workers to perform data labelling, content moderation, and model optimisation. Boards face reputational, regulatory, and supply-chain liability risks if workforce practices in AI procurement are not audited and disclosed.
AI Systems Developing Goals Misaligned with Human Values
General-purpose AI models may internalise objectives that diverge from human values, producing harmful or unpredictable behaviour at scale. Boards face direct liability exposure if deployed systems act contrary to public interest without adequate alignment controls in place.
AI Systems Widening Societal Inequality and Eroding Community Cohesion
AI and automation systems are amplifying disparities in wealth and social status between groups, undermining community cohesion and stability. Boards face mounting regulatory and reputational exposure as technology deployment without equity safeguards accelerates structural inequality.
AI Systems Accelerate Concentration of Economic and Political Power
AI and algorithmic tools are amplifying existing concentrations of economic and political power, compounding inequality and systemic instability. Boards must assess whether their AI deployments entrench market dominance or regulatory capture in ways that attract legislative intervention.
AI and Automation Systems Causing Direct and Indirect Environmental Damage
AI and automation systems generate environmental harms through energy consumption, hardware waste, and operationally driven ecological damage. Boards face growing regulatory and reputational exposure as sustainability obligations tighten around technology procurement and deployment.
AI-Driven Economic Instability Through Uncontrolled Financial System Fluctuations
Automated and algorithmic systems operating without adequate controls can trigger cascading, uncontrolled fluctuations across financial markets or critical economic infrastructure. Boards face systemic exposure where AI misuse precipitates instability that regulators and insurers will attribute to governance failures.
AI-Driven Micro-Targeting Used to Manipulate Voters via Personal Data
Retail platforms and loyalty data ecosystems become vectors for AI-powered political micro-targeting that exploits consumer behavioural profiles and personality vulnerabilities. Boards face regulatory exposure under data protection law and reputational risk if commercial data infrastructure is implicated in electoral manipulation.
AI-Driven Manipulation of Political Beliefs and Government Service Delivery
AI and algorithmic systems are being used to manipulate political beliefs and undermine public institutions, eroding democratic processes and the effective delivery of government services. Boards must treat political interference via automated systems as a material governance risk requiring active oversight and regulatory engagement.
Poorly defined operational boundaries disable autonomous vehicle safety testing
Autonomous transport systems with inadequately specified operational design domains cannot be reliably tested or monitored for out-of-distribution conditions. Boards risk approving deployments without verified safety envelopes, exposing operators to liability and regulatory censure.
AI and Automated Systems Causing Environmental Pollution
AI and automated systems have caused or risked actual pollution across air, ground, water, and noise environments. Boards face regulatory liability and reputational exposure where technology deployment lacks environmental impact controls.
AI-Generated Content Indistinguishable from Authentic Material
General-purpose AI systems produce synthetic content that cannot be reliably detected, compounding information integrity risks at scale. Boards face exposure to reputational, legal, and operational harm where provenance of content cannot be established.
Centralised AI Platforms Create Systemic Single Points of Failure
Widespread reliance on shared general-purpose AI platforms concentrates vulnerability, exposing interconnected systems to cascading disruption from a single attack or outage. Boards and regulators face amplified systemic risk where a failure in one provider propagates across entire technology sectors simultaneously.
AI-Generated Misinformation Degrades Student Learning and Institutional Trust
AI systems in education are producing and spreading false, hallucinated, or misleading content, corrupting the information environment students rely upon. Institutions face reputational damage, erosion of academic integrity, and regulatory scrutiny if governance frameworks fail to address AI-generated misinformation.
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