This is like an aviation incident log, but for AI: a central place where real-world AI failures, harms, and near-misses are collected, labeled, and analyzed so others can learn from them and avoid repeating the same mistakes.
Organizations deploying AI lack a structured, evidence-based view of how AI systems fail in the real world—across industries and use cases. This tool aggregates and categorizes AI incidents so leaders, regulators, and practitioners can understand common failure modes, benchmark their own risk controls, and design safer AI deployments (including in high-risk sectors like mining).
Curated, structured incident data and taxonomy developed by a reputable research institution (MIT), plus network effects as more organizations and researchers contribute and rely on the shared incident corpus.
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Unknown
Medium (Integration logic)
Data collection and curation volume/quality—scaling depends on continuous, reliable reporting and consistent taxonomy management rather than pure compute.
Early Adopters
Unlike generic AI risk commentary, this focuses on structured, incident-level evidence (who/what/where/why) that can be queried, analyzed, and used to inform concrete safety practices and governance frameworks across industries, including high-risk domains such as mining and heavy industry.