Beyond Accuracy: Identifying, Measuring and Mitigating AI Bias

About the webinar

Can AI be trustworthy? Yes. But first, you have to mitigate bias in machine learning models. We’ll show you SAS techniques and procedures that help data scientists identify, evaluate, mitigate and monitor bias in ML models. The result: more equitable and trustworthy AI solutions.

You will learn about:

• Common sources of bias in machine learning models.
• SAS procedures and metrics used to assess fairness.
• Practical approaches for monitoring and reducing model bias.

About the expert

Vrushali leads the practical implementation of fairness and trustworthy AI principles into the SAS platform. She bridges the gap between ethical AI theory and real-world application, ensuring responsible AI practices are embedded throughout technology development. As a thought leader in AI governance, Vrushali regularly writes and speaks about practical strategies for implementing trustworthy AI systems. Her work focuses on translating ethical frameworks into actionable guidance for building responsible AI solutions. With over a decade of experience in analytical consulting, data management and data visualization, she helps organizations implement ethical AI practices at scale. Vrushali holds master’s degrees in data science and business administration, combining technical expertise with strategic business acumen to address both technical and organizational aspects of responsible AI implementation.