I enthusiastically read Kush Varshney's book when it was released for free to the world several months back. Trustworthy Machine Learning (http://www.trustworthymachinelearning.com/) is a concise and clear overview of many of the ways that machine learning can go wrong, and so I was especially keen to get Kush (http://krvarshney.github.io/) on to talk more about his work and research.
I also got a stronger sense of appreciation for how good MLOps practices and workflows offered a clear path to ensuring that your machine learning models and behaviours could become more trustworthy. Kush has done a lot of interesting work, particularly with the AI Fairness 360 (https://ai-fairness-360.org/) and AI Explainability 360 (https://ai-explainability-360.org/) toolkits that I'm sure listeners of this podcast would find worth checking out. Special Guest: Kush Varshney.
