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AI can help recruiters source candidates, summarize applications, screen CVs, rank applicants or support interviews. The trust problem arises when candidates cannot tell what the system does, what information it uses or how much its output affects a decision. Survey evidence shows substantial candidate concern, but those perceptions do not, by themselves, prove that a particular hiring system is inaccurate or discriminatory.
What the candidate-trust figures show
Gartner’s findings point to a tension: candidates may use AI themselves while remaining uneasy about employers using it to evaluate them. The figures come from separate survey waves and samples, so they should not be combined or treated as one measure.
| Finding | Survey or study details |
|---|---|
| 26% trusted AI to evaluate them fairly; 32% were concerned AI could fail their applications; 25% said AI use lowered their trust in employers. | Gartner, 1Q25 survey of 2,918 job candidates. |
| 39% said they used AI during the application process. | Gartner, separate 4Q24 survey of 3,290 job candidates. |
| External and functional transparency reduced perceived differences in person-job fit. | 2025 experiment with 286 participants by Aihui Chen, Feifei Han, Xinyi Zhang and Yaobin Lu. |
| Almost 300 recommendations followed audits of AI recruitment providers and developers. | UK Information Commissioner’s Office (ICO), 2024. |
| More than 30 employers contributed evidence to later work on recruitment automation. | ICO engagement from March 2025 through January 2026, reported in 2026. |
These are findings about recruitment broadly, not IT recruitment specifically. They describe candidate responses, an experiment and regulatory work; they are not a technical audit of every hiring model or an estimate of how often AI causes a bad outcome.
Why trust and technical accuracy are different questions
A candidate’s confidence in a hiring process matters to whether that process feels understandable and legitimate. But trust is not a measurement of a model’s accuracy, and low trust is not proof that a system discriminates. The reverse is also true: a claim that AI is objective, consistent or fast does not establish that it treats applicants fairly.
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To assess a system, an employer needs to know what job-related criteria it applies, whether its outputs are suitable for the task, how performance and potential bias are checked, and what happens when the system produces an unsuitable result. Transparency can make those questions easier to ask; it cannot answer them on its own.
“AI recruitment” can mean assistance or a consequential decision
The label covers activities with very different effects on an applicant. An AI tool might help a recruiter find profiles or summarize applications, or it might filter out applications, rank candidates, assess interview responses or otherwise shape who advances. The practical question is not simply whether AI is present, but what stage it affects and how much its output influences the outcome.
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- Assistance: A system supplies information or a summary for a recruiter to consider. Its usefulness still depends on the quality and relevance of its output.
- Influence on selection: A score, filter or ranking may determine which applications receive attention or progress. Candidates may not see how much that output mattered.
- Automated decisions: A system may make or effectively determine a consequential selection outcome. The greater the effect, the more important it is to understand the criteria, oversight and routes for addressing errors.
A human reviewer is meaningful only if they have the training, time and authority to question the system’s output—not merely approve it by default.
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What makes disclosure useful to candidates
Useful transparency tells applicants what the tool does, when it is used, what information it considers and how its output contributes to a hiring decision. A vague statement that an employer “uses AI” leaves the candidate unable to understand the tool’s role. Clear explanations can help set expectations and shape perceptions, as the 2025 transparency experiment suggests, but its results do not prove that transparency makes a system unbiased or produces the same effect in every hiring context.
For an employer, a practical review should cover the full process rather than the model alone:
- Purpose and criteria: Identify the hiring stage affected and the job-related information and criteria used.
- Candidate communication: Explain the tool’s role and data use at a point when applicants can understand how it may affect them.
- Accessibility: Consider whether the process excludes people who need an accommodation or cannot use a particular digital format.
- Assessment: Evaluate performance and potential bias in the context in which the system is used; do not treat a vendor’s assurance as a substitute for assessment.
- Human oversight: Establish who reviews consequential outputs and how they can challenge or correct them.
- Personal data: Collect and retain only what is appropriate for the purpose, explain relevant data practices, and consider applicable candidate rights.
The UK Department for Science, Innovation and Technology’s 25 March 2024 guidance, Responsible AI in Recruitment, says: “As AI becomes increasingly prevalent in the HR and recruitment sector, it is essential that the procurement, deployment, and use of AI adheres to the UK Government’s AI regulatory principles.” The guidance identifies possible efficiency benefits as well as risks such as bias, digital exclusion, and discriminatory advertising or targeting. It recommends impact assessment and attention to accessibility and transparency.
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What UK and EU rules say—and where they apply
United Kingdom
The Department for Science, Innovation and Technology’s guidance is practical guidance, not a substitute for legal advice. The ICO is the UK data-protection regulator. Its 2024 audits of recruitment AI providers and developers produced almost 300 recommendations, including recommendations on fair and minimal personal-data processing and clear explanations for candidates. The ICO’s later engagement with more than 30 employers, spanning March 2025 to January 2026, is evidence of broader work on recruitment automation; it is not a finding that every employer or system has the same problem.
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The EU AI Act’s Annex III includes systems intended for recruitment or selection, including targeted job advertising, filtering applications and evaluating candidates, among its high-risk use cases. This is an EU-law classification, not a rule that automatically governs every employer worldwide. Whether particular obligations apply, and when, depends on the system and the relevant legal and implementation details; employers should check the current consolidated regulation and obtain jurisdiction-specific advice rather than assume a single date or duty covers every use.
What this means for IT hiring
There is no basis in the cited findings for claiming that IT candidates experience more AI-related unfairness than applicants in other fields, or that a particular employer or applicant-tracking system uses a specific model. The available candidate surveys and official guidance address recruitment more generally. Their relevance to IT hiring is that the same questions arise wherever AI helps select people for jobs: what is being assessed, whether the criteria relate to the role, whether applicants can use the process accessibly, and whether a qualified person can meaningfully review the result.
For candidates, it is reasonable to ask an employer what role an AI tool plays in the process and how to request an accommodation or raise a concern. For employers, the more defensible response to trust concerns is not a blanket claim that AI is fair, but a process that can explain its use, assess its effects and provide accountable human review.
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