Bias in Automated Hiring
Equip specialists to find, measure and fix bias in automated hiring systems: examine training data and labels, trace selection effects through the hiring funnel, calculate and interpret fairness metrics with appropriate caution, test systems directly, and recommend remediation that holds up.
Upcoming course. LMS delivery and enrolment are not open. Release dates will be confirmed.
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Planned learning outcomes
- Examine hiring training data and success labels for historical and measurement bias
- Trace selection bias through each stage of the hiring funnel, including drop-out
- Calculate stage-wise, cumulative and intersectional fairness metrics and judge their reliability
- Identify adverse outcomes for applicants and trace them to root causes
- Design counterfactual, ablation, robustness and accessibility tests for hiring systems
- Recommend and verify remediation, and report a hiring algorithm fairness review