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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. This guide provides a practical starting point to help development teams produce models that are secure, more robust, less biased, and more explainable.
About the author
Yada Pruksachatkun is a machine learning scientist at Infinitus, a conversational AI startup that automates calls in the healthcare system. She has worked on trustworthy natural language processing as an Applied Scientist at Amazon, and led the first healthcare NLP initiative within mid-sized startup ASAPP. She did research transfer learning in NLP in graduate school at NYU and was advised by Professor Sam Bowman.
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. 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.
Subhabrata (Subho) Majumdar is a Senior Applied Scientist at Splunk. Previously, he spent 3 years in AT&T, where he led research and development on ethical AI. Subho deeply believes in the power of data to bring about positive changes in the world---he has cofounded the Trustworthy ML Initiative, and has been a part of multiple successful industry-academia collaborations in the data for good space. Subho holds a PhD in Statistics from the University of Minnesota.
