Abstract: With the increasing use of AI in high-stakes domains such as medicine, law, and defense, organizations spend a lot of time and money to make ML models trustworthy. Many books on the subject offer deep dives into theories and concepts. This tutorial provides a practical starting point to help ML development teams produce models that are secure, more robust, less biased, and more explainable.
Drawing from our collective expertise and experience as trustworthy ML practitioners, we translate best practices in the academic literature for curating datasets and building models into a blueprint for building industry-grade trusted ML systems using a series of deep-dive, hands-on examples. All code used in this tutorial will be available on GitHub.
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