Practicing Trustworthy Machine Learning: A Tutorial


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.


Matthew McAteer is the creator of 5cube Labs, an ML consultancy that has worked with over 100 companies in industries ranging from architecture to medicine to agriculture to drug discovery. Matthew worked with the Tensorflow team at Google on probabilistic programming, and previously worked in biomedical research in labs at MIT and Harvard Medical School.

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