AI Bias, Fairness and Discrimination Auditing
Equip practitioners to identify where bias enters AI systems, test fairness with appropriate measures while understanding their limits, apply Australian discrimination and employment law considerations, and recommend practical remediation, monitoring and governance controls.
Upcoming course. LMS delivery and enrolment are not open. Release dates will be confirmed.
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Planned learning outcomes
- Explain how bias and unfairness arise in AI systems and why removing protected attributes does not remove bias
- Trace sources of dataset, model and deployment bias, including proxies, label bias and design choices
- Select and calculate fairness measures such as selection rates, impact ratios and error-rate comparisons, and explain their limitations
- Apply Australian discrimination, general protections and privacy considerations to AI used in recruitment and employment, with international comparison
- Recommend remediation, monitoring and governance controls proportionate to fairness risk
- Audit a simulated recruitment model and write evidence-based findings and recommendations