AI Builders Global Conference 2026. October 14-16, 2026. Online.
AI Bias Auditing at Scale: Detection, Measurement & Mitigation
This description belongs to the 2026 topic catalog. The 2027 program is being prepared separately.
Moving beyond theoretical ethics into practical implementation. This session covers how enterprises are building fairness testing into their AI pipelines, implementing bias detection at scale, and establishing algorithmic accountability frameworks. Featuring real case studies from finance (lending decisions), healthcare (clinical resource allocation), and hiring (resume screening): with specific tools, metrics, and methodologies that engineering teams can adopt immediately.
Questions explored in the 2026 topic catalog
- What bias metrics should you measure for different AI applications?
- How do you build automated bias detection into CI/CD pipelines?
- What tools and frameworks are available for fairness testing?
- How do you handle bias in training data vs. model decisions?
- What accountability structures work when AI produces biased outcomes?
- How do you communicate fairness metrics to non-technical stakeholders?
Who this topic is for
ML engineers, data scientists, ethics/responsible AI practitioners, product managers, and compliance officers responsible for AI fairness.
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