Group Fairness
Fairness criteria applied across groups defined by protected attributes, ensuring that statistical measures of outcomes are equitable across demographic categories.
In plain language
Making sure an AI system produces roughly equal outcomes (such as approval rates or error rates) across different groups like different genders, ages or cultural backgrounds. If the AI is more accurate for one group than another, that's a fairness problem.
Why this matters
Group fairness assessment is foundational for Australian AI governance. Organisations must track fairness metrics across all high-risk AI systems and establish escalation procedures when outcomes diverge across protected groups, both to meet emerging regulatory expectations and to mitigate discrimination risk.
Relevance
GovernanceGroup fairness metrics are essential for compliance reporting and discrimination risk management in regulated industries.
Related terms
Putting group fairness into practice in your organisation?
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