Original LinkedIn edition published
These are original companion notes. Read the full newsletter on LinkedIn
The short answer
Effective oversight requires recruiters to weigh a recommendation against its evidence and quality, then explain why they followed, changed or rejected it. A stated level of trust in the tool cannot establish how it influences the decision.
Test what happens when the recommendation disagrees
In the 2026 study behind this edition, 1,208 recruitment professionals made an initial choice between two candidates and then received conflicting advice. The researchers varied the source, accuracy and number of advisers. Recruiters reported less trust in the algorithmic system than in a human expert, while their choices were more influenced by its advice. The finding concerns this controlled decision task; it is not a measured rate of overreliance in live recruitment.
A hiring team needs a decision rule for disagreement before a tool enters the workflow. What evidence supports the initial judgement? What new information does the recommendation add? Are two agreeing signals genuinely independent or drawn from the same underlying data? Record the reason for following or overriding advice, then review patterns of errors, overrides and candidate outcomes. That gives the team evidence about the process it actually runs.
The Sova-delivered Core-6 assessment provides a common measure of foundational knowledge, including output evaluation, risk and human oversight. It does not observe a recruiter's decisions in a particular vacancy. An employer can separately use a structured discussion or a realistic case to examine how people check conflicting advice, without presenting that activity as a genAssess module or psychometric score.
Illustrative example: two recommendations from one source
A recruiter initially favours Candidate B on the agreed job criteria. Two dashboard indicators favour Candidate A, but both are derived from the same parsed CV data. Before treating the agreement as independent confirmation, the recruiter checks the source applications, the criteria used and the evidence missing from each summary. The recorded rationale explains which evidence changed the decision, if any, and who can challenge the outcome.
Questions to take into your next meeting
- Set the evidence criteria and initial decision before revealing the AI recommendation.
- Ask what new evidence the advice contains and whether agreeing signals share a source.
- Give recruiters time and authority to follow, modify or override advice with a recorded reason.
- Review conflicting cases for error patterns and unequal effects on candidate groups.
Put the idea to work
Review one disagreement from end to end
Take a recent AI-assisted recommendation that differed from a recruiter's initial view. Reconstruct what the recruiter knew at each step, what they checked and why the final decision changed or stayed the same. If the evidence trail is missing, add a short decision record and an escalation route before the next case.
When an AI recommendation challenges a recruiter's judgement, can your team show what actually decided the outcome?
Sources and further context
- Lacroux and Martin Lacroux, International Journal of Selection and Assessment (2026) Primary experimental study of advice taking among 1,208 recruitment professionals; the published abstract describes the trust–reliance finding and the limits of the task
- NIST AI Risk Management Framework Guidance on human roles, accountability and oversight in AI-enabled processes
Core-6 measures common AI knowledge through Sova. The activities discussed here are separate ways to explore work and behaviour.
How to define AI proficiency in hiring Try the separate A/A/H and PRIME worksheet See what Core-6 measures