Which database is designed to better anticipate and avoid risks of AI systems causing safety, fairness, or other ethical problems?

An incident database is specifically designed to collect, document, and analyze cases where AI systems have caused harm or exhibited problematic behavior, including issues related to safety, fairness, and ethics. By creating a public, structured repository of these events, researchers, developers, and policymakers can better understand the patterns of AI failures. This knowledge helps in anticipating and mitigating future risks, thereby improving the overall safety and trustworthiness of AI systems. The AI Incident Database (AIID) is a prominent example of such a resource.

A. Asset: An asset database tracks hardware or software inventory, not AI-specific ethical failures or safety incidents.
B. Code Repository: A code repository (e.g., Git) stores and versions source code but does not systematically track real-world AI incidents.
C. Configuration Management: A CMDB tracks the state and configuration of IT components, not the ethical or safety outcomes of AI systems.

- McGregor, S. (2021). Preventing repeated real-world AI failures by cataloging and analyzing incidents. Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 595-601. This paper describes the motivation and structure of the AI Incident Database. https://doi.org/10.1145/3461702.3462533

- Partnership on AI. (n.d.). AI Incident Database. The official project page states its goal is to "make it easier to learn from experience so we can prevent or mitigate bad outcomes." Available at: https://incidentdatabase.ai/