Learn without stopping at human error
Course overview · 4 min reading + 12 min practice, estimated
Principles and method
A review should explain the sequence, contributing conditions, detection gaps and impact. Human error is a starting description, not a sufficient cause. Ask why the system made the mistake easy and why controls failed to catch it. Prioritise actions that change the conditions and verify their effectiveness. Assign owners and dates, and track completion without confusing it with outcome. Share general learning while protecting sensitive details. Revisit whether the workflow should remain in scope; sometimes simplification or retirement is the strongest improvement.
Worked example
A reviewer approved a wrong attachment because the interface displayed only a filename shared by many records. The action adds identity context and binding checks rather than only telling reviewers to pay more attention.
Put it into practice
Write a short incident review with two contributing factors and three corrective actions.
Use fictional information and keep your work in your own notes.
Compare your approach: self-review guidance
For each action, state the mechanism it changes and the evidence that will show improvement. Include one system design change and a follow-up review date. Avoid blame as a substitute for analysis.
Sources and further reading
Original Academy teaching and fictional examples. These references provide context, not endorsement. Edition 2026.09; updated 2026-09-24.
- GOV.UK: Responsible AI in recruitment
UK guidance on procuring and deploying recruitment AI.
- NIST: AI Risk Management Framework
Voluntary framework for organising AI risks and controls.