From Code to Trust: Embedding Trustworthy Practices Across the AI Lifecycle


As AI systems permeate our lives, ensuring their robustness, fairness, and security is non-negotiable. But what does it mean for a technical person sitting behind the scenes writing code? How do you incorporate these practices while cleaning data, developing models, deploying, and managing AI systems? This session will delve into steps to get you started on your journey to building trustworthy AI systems, from a technical perspective. We will showcase how to operationalize the trustworthy AI principles into practice and share a starter guide for implementing trustworthy AI across the AI lifecycle.


Vrushali Sawant is a data scientist with SAS's Data Ethics Practice (DEP), steering the practical implementation of fairness and trustworthy principles into the SAS platform. She regularly writes and speaks about practical strategies for implementing trustworthy AI systems on and external platforms. With a background in technology consulting, data management, and data visualization, she has been helping customers make data driven decisions for a decade.

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