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Human + algorithmic judgment/4 min read/Direct sources linked

What recruiters trust and what they follow

In a resume-screening experiment, recruiters said they trusted human advice more. Their choices told a more complicated story.

What people said versus what they did

This was not a study of trust recovering after repeated mistakes. It compared recommendations from a human expert and a fictional algorithmic decision-support system, including recommendations that favored the less-suitable resume.1

The result was not simple algorithm aversion

Participants reviewed two resume summaries for an HR manager role. They received either no recommendation or a recommendation from a human expert or algorithmic system; some recommendations favored the less-suitable candidate. Recruiters trusted the human source more, but the inconsistent algorithm still affected their evaluations.1

That gap matters. Stated skepticism is not the same as resistance in the moment. A tool can look less trustworthy and still change a decision.1

For a candidate-facing product, the honest response is not "humans good, algorithms bad." It is to make automated feedback inspectable, bounded, and easy to challenge.

What this means in practice

RIYP interpretation: use automated feedback as a second set of eyes, not a verdict. Ask what evidence produced the judgment, whether the advice fits the role, and what would change the conclusion. If the system cannot answer those questions, confidence should stay limited.

Common questions

Does this mean scores are useless?

No. A score is useful only when you can see what drove it and what to change. It is a starting point for the review, not objective truth.

How should candidates respond?

Make your evidence easy to inspect: clear role context, specific ownership, supportable outcomes, and no claims you cannot defend.

How this shows up in your report

01

No all-knowing score

A number is useful only when you can trace it back to the resume and the criteria. It cannot stand in for an employer's decision.

02

Show the judgment behind the output

We surface the evidence behind feedback so you can accept, reject, or refine it instead of obeying a black box.

Sources and related research

Sources

  1. 01Should I Trust the Artificial Intelligence to Recruit? Recruiters' Perceptions and Behavior When Faced With Algorithm-Based Recommendation Systems During Resume Screening — Frontiers in Psychology (2022).

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