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Why are candidates wary of AI in recruitment?
Hiring tools can assist with sourcing applicants, summarizing applications, screening, ranking, or interviewing. Those uses are not all the same as letting a system make an automated decision. A tool may organize information for a recruiter to review, or it may have a more consequential role in filtering people out. Candidates have reason to want to know which is happening and whether a person can meaningfully reconsider an outcome.
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Gartner’s 1Q25 survey of 2,918 job candidates found that 26% trusted AI to evaluate them fairly. In the same survey, 32% were concerned AI could cause their applications to fail, and 25% said AI use lowered their trust in employers. These figures describe candidate perceptions; they do not measure a tool’s technical accuracy or establish discriminatory impact.
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What does transparency change—and what does it not prove?
Transparency can help candidates understand a tool’s role and how their information is used. A 2025 experiment by Aihui Chen, Feifei Han, Xinyi Zhang, and Yaobin Lu, involving 286 participants, found that external and functional transparency reduced perceived differences in person-job fit. This is evidence about perceptions in a particular experiment, not proof that transparency makes a system unbiased or that the same effect will occur in every hiring process.
Useful disclosure is more specific than a general statement that an employer uses AI. Candidates need to understand what stage the tool affects, what it contributes to the decision, and whether a human reviews its output. Employers also need job-related criteria, suitable data minimization, bias assessment, accessibility, and meaningful human review. Disclosure alone cannot substitute for those safeguards.
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What do UK guidance and EU law say?
UK: responsible use and data protection
The UK Department for Science, Innovation and Technology’s 25 March 2024 guidance, Responsible AI in Recruitment, recognizes possible efficiency benefits alongside risks such as bias, digital exclusion, and discriminatory advertising or targeting. It recommends impact assessment and attention to accessibility and transparency. The guidance states: “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.” This is practical guidance, not a substitute for legal advice.
The Information Commissioner’s Office (ICO), the UK data-protection regulator, reported in 2024 that audits of recruitment AI providers and developers led to almost 300 recommendations. These included processing personal data fairly and minimally and giving candidates clear explanations. In later work, the ICO said more than 30 employers contributed evidence through engagement from March 2025 to January 2026. These are UK regulatory and engagement findings; they should not be treated as evidence about every employer or recruitment system.
EU: recruitment systems listed as high-risk use cases
The EU AI Act’s Annex III includes AI systems intended for recruitment or selection, including systems for targeted job advertising, filtering applications, and evaluating candidates. That classification is specific to EU law; it does not mean the Act governs every employer worldwide. Which duties apply, and when, depends on the system and circumstances. Employers operating in or hiring into the EU should check the current consolidated regulation and obtain jurisdiction-specific advice rather than assume a single date or rule resolves every case.
How can an employer make an AI-assisted hiring process more trustworthy?
For an IT role, the central question is not whether a tool is labelled “AI,” but what it does to a candidate’s path through hiring. For example, summarizing a résumé for a recruiter and automatically filtering applications can have very different consequences. Employers can make the process easier to scrutinize by documenting the tool’s function, the job-related information and criteria it uses, and the human review around its outputs.
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- Define the tool’s role: Identify the hiring stage it affects and whether it advises, ranks, filters, or makes a decision.
- Use job-related criteria: Check that the information and criteria reflect the role’s actual requirements rather than convenient but weak proxies.
- Explain the process: Tell candidates when AI is involved, what it does, and how its output contributes to decisions.
- Plan for accessibility: Provide accessible ways to participate and appropriate accommodation options.
- Assess performance and bias: Evaluate outcomes against relevant criteria rather than relying on claims of objectivity or efficiency.
- Make human review meaningful: Ensure reviewers can understand and question an output instead of simply accepting it.
- Limit and govern data: Retain only appropriate information, explain its use, and make applicable candidate rights clear.
These checks do not guarantee a fair result. They make the system’s role and governance more visible, and give employers a basis for examining whether the process is appropriate for the hiring decision.
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A candidate can ask whether AI is used at any stage, what task it performs, whether it can screen out an application, and whether a recruiter reviews its output. It is also reasonable to ask what information is assessed, what accessibility or accommodation options are available, and how personal data is handled. A clear answer does not establish that the system is fair, but it helps reveal whether the employer can explain and take responsibility for the process.
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