Trustworthy Machine Learning

We will discuss the concepts for developing accurate, fair, robust, explainable, transparent, inclusive, empowering, and beneficial machine learning systems. Accuracy is not enough when you?re developing machine learning systems for consequential application domains. You also need to make sure that your models are fair, have not been tampered with, will not fall apart in different conditions, and can be understood by people. Your design and development process has to be transparent and inclusive. You don?t want the systems you create to be harmful, but to help people flourish in ways they consent to. All of these considerations beyond accuracy that make machine learning safe, responsible, and worthy of our trust have been described by many experts as the biggest challenge of the next five years.