Candidate sentiment toward AI in hiring is mixed. A 2025 Gartner survey found that only 26% of candidates trust AI to evaluate them fairly, yet over half (52%) believe AI is already screening their applications.

The more important finding is this: distrust is largely about perception, not experience. Candidates who haven't yet gone through AI screening worry about it more than those who already have. The concern centres on the black box: a faceless algorithm making a consequential decision with no explanation and no feedback. That concern is rational, and it describes real problems with poorly designed tools.

It doesn't describe well-designed ones like Hubert's.

It's also worth noting that candidates aren't necessarily avoiding AI. On the contrary, they're using it themselves. The same Gartner survey found that 39% of candidates used AI during the application process to draft CVs, cover letters, and assessment answers. This shows that the relationship between candidates and AI in hiring is more active than passive.

The concerns come up consistently across research. Fairness: will the AI judge me on how I phrase things, my accent, or my background? Transparency: will I know what I'm being assessed on? Feedback: will I get any, or just silence? Human oversight: does anyone actually review my answers?

These aren't unreasonable concerns, they describe real problems with poorly designed tools. And the pattern is clear: candidates aren't asking for AI to disappear from hiring. They're asking for honesty about how it's used, and assurance that a person is still in the loop before a decision is made.

The Greenhouse research from 2026 backs this up. Only 19% of candidates want less AI in hiring. The majority want the same or more, but with guardrails such as upfront disclosure about AI use (44%) and a clear explanation of what the AI is measuring (39%). They also want accountability: 38% want to know a human reviews the AI's evaluation before any decision is made, and 29% want evidence the tool has been audited for bias.

That's a reasonable ask and it's one that well-designed AI screening can meet.

The biggest shift in candidate attitude happens after the interview, not before. Candidates who complete a well-designed, structured AI screening consistently report a better experience than they expected going in.

Why? Because the variables that drive negative sentiment such as opacity, no feedback, feeling processed rather than assessed, are design choices. They're not inevitable features of AI screening. When the process is clear, fast, and fair, candidates respond accordingly.

Hubert's AI screening interviews average a 9/10 candidate satisfaction score and a 96% interview completion rate. On Google, Hubert is rated 4.9 out of 5. Candidates say it best themselves:

"The application process was very smooth and user-friendly. The digital mini interview was a great idea and made the process quick, easy and engaging. Overall, it was a very positive experience." ★★★★★

"I really got the chance to think through my answers without feeling stressed." ★★★★★ (translated from Swedish)

"Excellent experience. I'd recommend it 100%." ★★★★★ (translated from Spanish)

Those aren't vanity metrics, they are real reviews that reflect what happens when a screening tool is built around the candidate, not bolted onto a recruiter workflow.

Convenience is a bigger driver of positive AI screening sentiment than most hiring teams realize. Candidates value being able to complete a screening at a time that works for them and not when a recruiter has a free slot on a Wednesday afternoon.

More than 60% of interviews completed across ManpowerGroup's Hubert deployment happened outside traditional office hours. Around 70% of candidates complete their screening on a mobile device. The median time from receiving an interview invitation to starting it is one minute.

For candidates in frontline, retail, logistics, and care roles (many of whom are applying while working other jobs or managing family commitments) that accessibility isn't a minor convenience. It's a meaningful signal that the employer respects their time.

When candidates report a bad AI screening experience, the patterns are consistent. They weren't told they were being assessed by AI, no feedback was given after the process, and the system felt impersonal or sloppy.

The scale of this is significant. Greenhouse research found that 70% of candidates were never clearly told upfront that AI would be evaluating them, and one in five only found out once the interview had already started. That's not a candidate experience problem. That's a transparency problem, and it's fixable.

Hubert's approach addresses each of these points directly. Candidates are told they're completing an AI-assisted interview every time. Every score is tied to a specific response with a full audit trail and no black box. Recruiters receive fully explainable shortlists, and every candidate receives personalized feedback after completing the interview, regardless of outcome. It's also the approach the EU AI Act now requires; classifying employment screening AI as high-risk, making human oversight and explainability legal obligations, not optional extras.

One concern that persists in candidate research is whether AI screening is actually fairer, or just automates the same bias that lives in CV reviews. That skepticism is well-founded for tools that screen on keyword matches and historical hiring patterns.

It doesn't apply the same way to structured, competency-based interviews, and candidates who go through one can tell the difference. Being asked specific, job-relevant questions about real situations, and knowing your answer is assessed against clear criteria, feels fairer than being filtered on whether your education or job history fits a pattern.

NSS Group found that 50% of hires made through Hubert came from candidates who wouldn't have passed traditional CV screening. For candidates from non-traditional backgrounds, a skills-based AI interview isn't a threat. It's a more level playing field.

Framing this clearly to candidates before they start reduces anxiety and increases completion rates. Candidates who understand they're being assessed on job-relevant competencies (and not filtered on CV keywords) approach the process with more confidence and less apprehension

The research points in a consistent direction. Candidate acceptance of AI screening isn't about whether AI is used, it's about how. The variables that drive a best-in-class candidate experience are transparency, consistency, speed, accessibility, and a human in the loop at the decision stage.

The variables that drive negative experience are the opposite, and they're all fixable.

If you want to see what a 9/10 candidate experience looks like at scale, book a demo with Hubert or explore the platform.