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Bias & Fairness

The Bias & Fairness community tackles one of the hardest challenges in AI — ensuring models treat all users equitably. From designing bias audits that catch subtle proxy discrimination to defining fairness metrics that are meaningful in practice, this community brings together researchers, ethicists, engineers, and compliance professionals working to eliminate harmful bias from AI systems.

Fairness metricsBias auditingProtected attributesIntersectionality

Detecting proxy discrimination in lending models — name and zip code bias

@fairness_researcher·29 replies·2 hours ago

Fairness metrics comparison: demographic parity vs equalised odds

@ml_ethics·21 replies·5 hours ago

How we audit our customer service AI for gender bias monthly

@trust_safety·17 replies·1 day ago

Intersectional bias testing — compounding effects across demographics

@dei_lead·24 replies·1 day ago

Building inclusive evaluation datasets that represent diverse populations

@data_curator·11 replies·2 days ago

Regulatory requirements for bias testing — EU AI Act Article 10

@compliance_eng·15 replies·3 days ago

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