Identifying HR bias

How can you establish verifiable findings on bias in an HR process without delegating decisions to AI?

1960 € per session, excl. VAT

In short

This 7-hour day, excluding breaks, enables you to conduct a structured audit of bias risks in a recruitment process. Generative AI helps examine documents and formulate hypotheses, which are then verified. You produce a risk matrix, an evidence file and measurable corrective actions.

Why this training?

A job advert, a shortlisting criterion or an interview assessment can introduce disadvantages without explicit intent. The audit must distinguish between a risk signal, an observed difference and a substantiated conclusion. AI can speed up the cross-referencing of documents, but can also reproduce stereotypes or invent a justification. Source traceability and independent checks prevent its suggestions from being turned into verdicts. Running case study: The fictional organisation ‘Virelune Instruments’ is recruiting technicians. Comparable applications receive differing assessments depending on their backgrounds. The file includes a job advert, an interview assessment grid, fictional applications and a simulated shortlisting history. Transfer to the workplace: Reuse the grid on a defined HR process, with authorised anonymised data. Have the findings validated by a qualified HR professional before any correction, then compare the indicators at the next review.

Objectives of this training

  • Map the risk points in a recruitment journey
  • Link each bias hypothesis to accessible evidence
  • Test the consistency of a grid on comparable applications
  • Calculate descriptive differences while making their limitations explicit
  • Prioritise corrections with monitoring indicators

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • HR and recruitment managers responsible for reviewing their selection practices
  • Diversity leads and internal auditors working on HR processes

Prior skills

  • Know the stages of recruitment and the assessment criteria used in HR
  • Be able to formulate a request to generative AI and check its response
  • Use filters and calculate proportions in a spreadsheet

What you will work on

  1. Define the recruitment process under review

    Objective: Set a workable audit scope. Stages, stakeholders, explicit criteria and scope for judgement; no automated sensitive decisions.

  2. Prepare the supporting evidence

    Objective: Compile a traceable file. Minimisation, anonymisation, indirect variables and missing information; conditions of use to be checked with your legal department.

  3. Examine the written criteria

    Objective: Identify potentially unjustified requirements. Wording bias, qualifications substituted for skills, availability and atypical career paths; limitations of generated suggestions.

  4. Test the consistency of assessments

    Objective: Identify unjustified variations. Comparison of fictional files with equivalent skills; modification of a single irrelevant cue; repetition and human checking.

  5. Consolidate the findings

    Objective: Distinguish between a descriptive difference and evidence of bias. Progression rates, denominators, small numbers and confounding factors; no inference of sensitive characteristics.

  6. Organise corrective action

    Objective: Make recommendations verifiable. Revision of criteria, harmonisation of interviews, severity and priority; validation by a qualified HR professional.

What you take away

  • Map of the scope and bias risks
  • Register of evidence, hypotheses and limitations
  • Revised recruitment grid and checking protocol
  • Prioritised corrective action plan with owners and indicators

Method and assessment

  • Short inputs followed by immediate practice on the running case study.
  • Individual work followed by work in pairs, with peer review of outputs.
  • Guided group discussion: what works, what needs correcting and what remains a human decision.
  • Preparation: Arrange access to a generative AI assistant authorised by the organisation and a spreadsheet application. Choose an HR stage to examine; the exercises use only the fictional file provided or approved anonymised documents.

Observable criteria: Each retained risk refers to a verifiable extract or calculation. ; Differences are recalculated with explicit counts and denominators. ; Conclusions distinguish between facts, hypotheses and insufficient data. ; Corrections are based on job-related criteria. ; The plan provides for human validation and an indicator for each action..

Frequently asked questions

Can AI conclude that discrimination has occurred?

No. It suggests lines of enquiry; determining whether discrimination has occurred requires expertise and, if necessary, a legal review.

Can real files be used?

Only if anonymised and authorised. No files identifying individuals are needed for the exercises.

Do sensitive characteristics need to be collected?

Not for this day. Any collection envisaged later must be checked with your legal department.

Does the method apply to promotions?

Yes, by adapting the criteria, evidence and indicators to the mobility process concerned.

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