Applied Data Science Invited Speakers

The Applied Data Science Invited Talks will provide a venue for leading experts in the world of applied data mining and knowledge discovery. These invited talks will feature highly influential speakers who have directly contributed to successful data mining applications in their respective fields. The talks and discussions will focus on innovative and leading-edge, large-scale industry or government applications of data mining in areas such as finance, health-care, bio-informatics, public policy, infrastructure, telecommunications, social media and computational advertising.


Keynote: Saleema Amershi

Saleema Amershi

Microsoft Research

Toward Responsible AI by Planning to Fail

"The potential for AI technologies to enhance human capabilities and improve our lives is of little debate; yet, neither is their potential to cause harm and social disruption. While preventing or minimizing AI biases and harms is justifiably the subject of intense study in academic, industrial and even legal communities, an approach centered on acknowledging and planning for AI-based failures has the potential to shed new light on how to develop and deploy responsible AI-based systems. In this talk, I will discuss the sociotechnical nature of several inherent and unavoidable AI failures and why it is important for the industry to systematically and proactively identify, assess, and mitigate harms caused by such failures in our AI-based products and services. I will then present Microsoft's recently released Guidelines for Human-AI Interaction and how we've been using them at Microsoft to help teams think through and prepare for different types of AI failures."

Saleema Amershi is a Principal Researcher at Microsoft Research AI and currently co-chair Microsoft’s Aether Working Group on Human-AI Interaction and Collaboration. Aether is Microsoft’s advisory committee on responsible and ethical AI. Her research focuses on helping people create effective and responsible AI user experiences. Her recent work includes leading Microsoft’s effort to develop general Guidelines for Human-AI Interaction, a unified and validated set of guidelines to establish a foundation for human-AI interaction design. Throughout the years, she have developed tools and methodologies to support practitioners in designing and building AI-based products and services, including general purpose platforms and visualizations for data scientists building predictive models, and application specific techniques for supporting end-users interacting with AI-systems in their everyday lives. She hold a PhD in Computer Science & Engineering from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Prior to UW, She completed a MSc in Computer Science and a BSc in Computer Science & Mathematics at the University of British Columbia.