Failure-mode taxonomy
53 Ways This Could Go Wrong
Fifty-three documented ways AI systems — or the way we deploy them — can produce harm, mapped across eight mechanistic categories. Each is a distinct pathway, not a bug. Colour marks the category; brightness marks how much evidence exists, from demonstrated, to observed, to merely projected.
Failure-mode taxonomy
53 Ways This Could Go Wrong
Documented failure modes across 8 categories — from training dynamics to physical-world risks. Each represents a distinct pathway by which AI systems or their deployment can produce harm. Color indicates category; brightness indicates evidence strength.
53 documented mechanisms through which AI systems can cause harm, organized by category. Click any cell to see real-world examples and the chain of causation.
Compound Risks
When failure modes from different categories combine, they produce emergent risks not predictable from either component alone. Click a compound risk to highlight the connected mechanisms above.
Taxonomy: 53 failure modes across 8 mechanistic categories, derived from technical literature and empirical observation through early 2026
Evidence levels — Demonstrated: observed in deployed systems or experiments. Observed: evident in research settings or early deployment. Projected: theoretically grounded but not yet empirically confirmed.
Recap
- What you saw
53 documented AI failure modes across 8 mechanistic categories.
- What it means
AI risk is not one question but a field with its own atlas — and most of the map is already charted.
- What to watch
New entries added to the MIT AI Risk Repository, especially at the Demonstrated level.