AIAAIC Repository

Summary

The AIAAIC Repository stands out as the most comprehensive global database tracking AI incidents and controversies across industries and jurisdictions. Unlike theoretical frameworks or compliance checklists, this repository documents real-world failures, near-misses, and ethical controversies involving AI systems. Each incident includes detailed timelines, stakeholder responses, media coverage, and long-term outcomes—making it an invaluable resource for understanding how AI governance plays out in practice.

What makes this different

Most AI governance resources tell you what should happen. The AIAAIC Repository shows you what actually does happen when AI systems go wrong. It captures incidents ranging from algorithmic bias in hiring systems to autonomous vehicle crashes, from facial recognition controversies to deepfake political manipulation. The repository doesn't just catalog failures—it tracks the complete lifecycle of incidents, including regulatory responses, public backlash, and corporate accountability measures.

The database structure allows for sophisticated analysis across multiple dimensions: by industry (healthcare, finance, criminal justice), by harm type (discrimination, privacy violations, physical safety), by geography, and by the AI system's maturity level. This granular approach makes it possible to identify patterns and predict where future incidents might occur.

Learning from failure: How to use this resource

• Pattern recognition: Use the repository to identify recurring failure modes in your industry or use case. If you're deploying facial recognition, study the documented controversies to understand common pitfalls and stakeholder concerns.
• Risk assessment: The incident timelines reveal how quickly problems can escalate and which types of failures generate the most regulatory and public attention. This intelligence is crucial for prioritizing risk mitigation efforts.
• Stakeholder mapping: Each case study documents who gets involved when things go wrong—regulators, advocacy groups, media outlets, and affected communities. Understanding this ecosystem helps you prepare more effective response strategies.
• Accountability benchmarking: The repository tracks how different organizations respond to incidents, from denial and deflection to proactive remediation and policy changes. These real-world examples provide templates for developing your own incident response protocols.

Who this resource is for

• Risk managers and compliance teams will find this invaluable for building comprehensive risk registers and testing incident response plans against real-world scenarios.
• AI product managers and engineers can use the documented failures to inform design decisions, testing protocols, and deployment strategies—essentially learning from others' expensive mistakes.
• Policy makers and regulators gain insights into emerging patterns of AI harm and the effectiveness of different regulatory responses across jurisdictions.
• Researchers and journalists investigating AI governance will find the repository's detailed documentation and cross-referencing superior to piecing together information from scattered news reports.
• Legal teams can study how liability questions have been resolved in similar cases and understand the evolving legal landscape around AI accountability.

The real-world impact story

The repository has documented several landmark cases that fundamentally shifted AI governance conversations. The Cambridge Analytica incident traced in the database helped catalyze global data protection reforms. Documentation of biased hiring algorithms led to new audit requirements in multiple jurisdictions. The comprehensive tracking of autonomous vehicle incidents has informed safety standards still being developed today.

What makes these case studies particularly valuable is their longitudinal perspective—you can see how incidents that seemed minor at first sometimes triggered major regulatory changes, while others that generated significant media attention ultimately had little lasting impact.

Watch out for

The repository's global scope means incident reporting varies significantly by region. Some jurisdictions have mandatory incident reporting requirements that generate more comprehensive documentation, while others rely heavily on media coverage and whistleblower reports.

The database focuses on publicly known incidents, which may not represent the full universe of AI failures. Organizations often resolve problems quietly or may not even recognize certain types of algorithmic harm.

While the repository attempts to track long-term outcomes, the AI governance landscape evolves rapidly. Regulatory responses documented in older cases may not reflect current enforcement approaches or penalty structures.