AI
Human-in-the-Loop AI for Hiring Decisions
Why AI should recommend and people should decide.
By Prakura AI Team · · 7 min read
The problem with automated ranking
Ranking models can encode patterns from historical data that we do not want to repeat. When a ranking becomes a decision, nobody is accountable for it.
The safer pattern is to treat AI output as a recommendation with visible reasoning, and to require a person to make the final call.
The recommendation → review → decision pattern
Every AI suggestion carries its inputs, a confidence label and an explanation. A reviewer can accept, edit or reject it, and that action is logged.
- Show the evidence behind a recommendation
- Never auto-reject candidates
- Log who made the final decision
- Review outcomes for unintended bias
Designing the review UX
Good review interfaces make disagreement easy. Put the human action first, keep AI reasoning one click away, and avoid colour-only signals for scores.
What comes next
We are applying the same pattern to placement readiness and performance insights across Prakura products.