AI Recruitment Assurance

Why conduct an AI Bias Audit?

AI recruitment technology can improve efficiency and consistency, but it can also introduce unintended risks if left untested.

An independent audit helps your organisation:

  • Validate whether AI scoring is fair across demographic groups

  • Identify evidence of adverse impact or algorithmic bias

  • Measure consistency and reliability of AI-generated scores

  • Compare AI recommendations against human assessors

  • Understand whether AI is improving hiring decisions

  • Demonstrate responsible AI governance

  • Build confidence among candidates and stakeholders

  • Support compliance with emerging AI regulations and organisational policies

What we evaluate:

Our audits assess AI recruitment technology across four key areas.

Artificial intelligence is transforming recruitment, but implementing AI responsibly requires more than vendor claims and marketing material.

Talogic provides independent AI Bias Audits that evaluate whether AI-enabled recruitment tools are fair, reliable and suitable for use in employee selection. Using evidence-based assessment methods, we help organisations understand how AI systems perform across different demographic groups, whether they produce consistent results and how confidently they can be used in hiring decisions.

Whether you're introducing AI into your recruitment process for the first time or reviewing an existing implementation, we provide practical recommendations backed by your data.

  • We evaluate whether candidate outcomes differ across demographic groups using recognised statistical methods including adverse impact analysis, effect sizes and significance testing.

    Typical analyses include:

    • Four-fifths (80%) Rule

    • Adverse Impact Ratios

    • Chi-square and Fisher's Exact Tests

    • Cohen's d

    • Selection rate comparisons

    • Decision reversal analysis

  • We assess whether AI-generated scores meaningfully predict recruitment outcomes and whether the AI is measuring the capabilities it claims to measure.

    Depending on available data, analyses may include:

    • Construct validation

    • Criterion-related validity

    • Predictive validity

    • Concurrent validity

    • Incremental validity

    • Correlation with human assessments

  • Responsible AI isn't only about statistics.

    We review the broader recruitment experience including:

    • Transparency

    • Candidate communications

    • Accessibility

    • Reasonable adjustments

    • Privacy considerations

    • User experience

  • We review whether appropriate governance exists around the implementation and ongoing use of AI.

    Areas include:

    • Human oversight

    • Auditability

    • Documentation

    • Monitoring processes

    • Decision review procedures

    • Vendor governance

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