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.
Generative AI Systems Amplify Societal Bias and Suppress Output Diversity
Generative AI models trained on non-representative data reproduce and intensify historical biases, creating measurable performance disparities across demographic groups and languages. Organisations face legal exposure, reputational harm, and flawed decision-making where homogenised outputs go unchallenged in operational processes.
Advanced AI Loss of Control Risk Identified in International Safety Report
International scientific assessment warns that advanced AI agents may reach a point where societal constraints become ineffective, even when harm is evident. Governments and boards face urgent pressure to establish oversight mechanisms before delegation of decisions to AI systems becomes irreversible.
Generative AI Lowers Barrier to Large-Scale Disinformation Campaigns
Generative AI enables bad actors to produce and distribute misleading content at scale, blurring the line between fact, opinion, and fiction. Boards face material exposure to reputational, regulatory, and market integrity risks where AI-amplified disinformation distorts investor decision-making.
AI Systems Enabling Escalating Privacy Violations and Surveillance
AI capabilities are outpacing privacy safeguards, enabling mass surveillance, automated data theft, and experimental thought-decoding by state actors. Boards face regulatory exposure under data protection frameworks and reputational risk as these techniques proliferate into commercial and law-enforcement contexts.
AI Systems Acquiring Autonomous Agency Beyond Passive Tool Classification
Advanced AI is transitioning from passive instrument to autonomous agent, capable of independent initiative and goal pursuit without human control. Existing regulatory and governance frameworks built on the passive-tool assumption are structurally unfit for oversight of agentic AI behaviour.
Concentrated AI Supply Chain Creates Systemic Risk in Healthcare Sector
A handful of technology firms control the general-purpose AI models underpinning critical healthcare operations, creating single points of failure with sector-wide consequences. Boards face both operational continuity risk and governance exposure if a dominant provider suffers outage, breach, or regulatory action.
Systemic Gap in Human-AI Coexistence Frameworks Across Public Sector
Seventeen percent of AI ethics literature identifies the absence of structured human-machine coexistence frameworks as a critical systemic risk. Governments lacking such frameworks face compounding accountability gaps as AI deployment accelerates across public services.
Accountability Gaps in AI Decision-Making Systems
AI systems cannot reliably replicate the contextual, moral, and empathetic dimensions of human accountability, leaving automated decisions structurally ungovernable. Governments deploying AI in public services face legal exposure and democratic legitimacy risks where no accountable agent can be identified.
AI R&D Concentration Deepens Global Technology Inequality
Advanced AI development is consolidating among a handful of Western nations and China, driven by compute barriers that exclude low-income countries entirely. This structural divide amplifies existing socioeconomic disparities and concentrates market power within large technology firms, heightening regulatory and reputational risk for global businesses.
Black-Box AI in Healthcare Blocks Clinical Verification
AI systems used in medical decision-making cannot be verified due to their opaque, non-linear structures, leaving clinical outputs without audit trails. Regulators and boards face direct liability where unverifiable AI informs patient care.
AI Systems in Elder and Child Care Risk Psychological Manipulation
Advanced AI deployed in elder and child care settings presents documented risks of psychological manipulation and clinical misjudgement of vulnerable users. Boards face mounting liability exposure and regulatory scrutiny where duty-of-care obligations intersect with autonomous system deployment.
Autonomous Vehicle AI Creates Unresolved Liability and Ethical Decision Gaps
Autonomous transport AI lacks settled frameworks for allocating accident liability and encoding ethical decision logic in life-critical scenarios. Governments and operators face regulatory exposure and public trust risk until clear accountability structures are legislated.
Generative AI Reproduces Copyrighted and Proprietary Content Without Authorisation
Generative AI systems lower the barrier to reproducing copyrighted, trademarked, or licensed material and exposing trade secrets at scale. Organisations face direct legal liability, reputational damage, and potential regulatory action if AI outputs infringe third-party intellectual property rights.
Absence of Accountability Frameworks in AI Decision-Making
AI systems making consequential decisions without clear procedural or substantive standards create governance vacuums where responsibility cannot be assigned. Boards face direct liability exposure when no accountable party can be identified following a harmful or non-compliant AI output.
General-Purpose AI Repurposed for Malicious Ends
Advanced general-purpose AI systems can be redirected toward harmful applications across a broad range of knowledge domains, including emerging threat vectors not yet fully evidenced. Boards must establish governance frameworks now to pre-empt liability exposure as regulatory scrutiny of malicious-use scenarios intensifies.
AI in Elder and Child Care Raises Manipulation and Privacy Governance Risks
AI systems deployed in elder and child care carry documented risks of psychological manipulation and clinical misjudgement, while AI-driven medical research exposes patient data to inadequately governed privacy risks. Boards face mounting regulatory and reputational liability without robust data governance frameworks and patient rights protections in place.
Superintelligent AI Agents Risk Becoming Uncontrollable as Autonomy Increases
Advanced AI agents operating at high autonomy levels may exceed human capacity to oversee or intervene, a problem research indicates has no reliable technical solution. Governments and boards that deploy autonomous AI systems without enforceable control mechanisms face irreversible loss of operational oversight.
AI Decision Opacity Blocks External Accountability in Public-Sector Systems
AI models in government ecosystems cannot reliably show external parties which data inputs drove a given decision. This opacity undermines regulatory scrutiny, legal challenge rights, and public trust in automated public-sector processes.
LLMs Accelerating Dual-Use AI Development at Scale
Large language models can autonomously build new AI systems and adapt existing ones for high-risk applications, compressing development timelines. Boards face material liability exposure as dual-use capability proliferation outpaces regulatory oversight and internal governance controls.
AI Systems Exposing Personal Data Through Inadequately Secured Channels
AI deployments create exploitable vulnerabilities through which personal information can be accessed without authorisation. Boards face regulatory exposure and loss of user trust where data governance frameworks fail to address AI-specific privacy risks.
AI Systems Driving Unquantified Environmental and Climate Harms
General-purpose AI deployment generates material environmental risks including accelerated energy consumption, carbon emissions, and pollution at scale. Boards lack adequate disclosure frameworks to assess or govern these liabilities, creating regulatory and reputational exposure.
AI Systems Exposed to the Same Cyber Vulnerabilities as Conventional Software
Learning systems carry identical attack surfaces to standard software, making them susceptible to exploitation, data poisoning, and adversarial manipulation. Boards must treat AI components as critical cyber assets within existing security governance frameworks.
AI Systems Drive Excessive Energy Consumption and Supply Shortfalls
Unconstrained AI and automation workloads consume disproportionate energy, creating bottlenecks that deprive communities and businesses of reliable supply. Boards face regulatory scrutiny and reputational risk if energy governance frameworks fail to account for AI infrastructure demand.
AI and Automation Systems Drive Excess Carbon Emissions
AI and automation deployments generate substantial carbon dioxide and related emissions, worsening climate change and harming local communities. Boards face growing regulatory and reputational exposure as environmental costs of AI infrastructure attract scrutiny.
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