We are living at a time when computer systems are increasingly data-driven, and increasingly powerful. They are improving our lives and enhancing our ability to solve complex problems. But they also can exhibit bias, leading to unfair results, or make mistakes, potentially harming us. To counteract these issues, researchers at the University of Massachusetts Amherst have embraced a vision of “Computing for the Common Good”: computing that not only may be used for good, but that is also, intrinsically, good – fair, accessible, explainable, trustworthy, effective and efficient. In this talk, I will discuss this vision and particularly, its applications to data science, illustrate it in action with examples of ongoing research and educational programs, and end with a few thoughts on open research challenges.

Laura Haas is the Dean of the College of Information and Computer Sciences at the University of Massachusetts Amherst. She was formerly an IBM Fellow and the founder and director of IBM Research’s Accelerated Discovery Lab. She held a broad range of positions at IBM in both research and development divisions, and is best known for her work on systems and tools for integrating data that may originate in very different systems, be represented in diverse formats, and be of a variety of types. Dr. Haas has received the Anita Borg Institute Technical Leadership Award, the ACM SIGMOD Codd Innovation Award and several IBM awards including a Corporate Award for information integration technology. She has served as Vice President of the VLDB Endowment Board of Trustees and as Vice Chair of the Computing Research Association board; she currently chairs the National Academies’ Computer Science and Telecommunications Board. She is an ACM Fellow, a member of the National Academy of Engineering, and a Fellow of the American Academy of Arts and Sciences.