Distinguished Speaker Series - "Engineering Responsible AI"

Recent achievements and advancements in AI have outpaced what anyone would have thought imaginable even five to ten years ago. For instance, we are fast approaching human parity across many tasks of AI — speech, vision and natural language, on many benchmark data sets. But many practitioners building AI technology and deploying AI products have not always thought through the societal implications such as fairness and transparency. Recently, a number of principles have been proposed to help companies and countries navigate the complexities and implications of AI. But principles alone are no longer enough — industry, academia and government need to take actions now to move from principles to practices. In this lecture, the speaker will share some examples of what have been practiced in Microsoft AI and Research: from doing research in explainable and interpretable AI, to creating tools for bias error analysis in machine learning, to debiasing word embedding learnt from the web.