Run a post-error AI review
Conduct a post-error review that updates AI reliance boundaries and ownership.
A resume-screening assistant missed qualified internal applicants with older job titles. Post-error review: classify, bound, assign, monitor The common trap is deciding the AI is either bad or fine without identifying the failed case class and changing the reliance boundary. Classify Classify the miss as an input and labeling issue: older internal titles did not map to the skills the role required. The cause matters because a model weakness, stale data, bad input, and misuse require different fixes. Bound Create a boundary: internal applicants and title-mismatch cases require human review before rejection. This preserves useful automation for routine cases while…
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