Scientific inquiry has always depended on data and various manifestations of data science. The nature of that reliance, however, has metamorphosed dramatically in the 21st century. An unprecedented quantity and breadth of information, the ability to share data efficiently among disciplines, ever-expanding computational power, and the democratization of algorithms across domains continue to revolutionize the scientific landscape. Still largely absent, though, are systematic approaches to using big data to solve the most urgent societal challenges across multiple domains. This new book chronicles initiatives underway at Massachusetts Institute of Technology (MIT) and elsewhere to address that deficit. Our goal in this book is to share key lessons we’ve learned through the launch of a new transdiscipline of Data, Systems, and Society that applies pioneering technologies to complex challenges. In doing so, we hope to encourage academicians, practitioners, students, and funders to join a growing worldwide effort to use data science and AI for societal good. Our story will explore pivotal themes in the history of computing, data science, systems thinking, and the social sciences. These themes contribute to the development of new methodologies and mindsets necessary to solve previously intractable societal problems. In this talk, I will introduce the topics in this book. I will illustrate how bias, algorithmic fairness, causality, privacy, and statistical accuracy all play crucial systemic roles in addressing societal challenges. I will conclude by outlining the structure and evolution of the new entity (IDSS), as well as showcasing breakthroughs resulting from our new transdisciplinary approach, highlighting the promise of innovative thinking and interventions.
Munther A. Dahleh received his B.S. in Electrical Engineering from TAMU in 1983, Ph.D. degree from Rice University, Houston, TX, in 1987 in Electrical and Computer Engineering. Since then, he has been with the Department of Electrical Engineering and Computer Science (EECS), MIT, Cambridge, MA, where he is now the William A. Coolidge Professor of EECS. He is also a faculty affiliate of the Sloan School of Management. He is the founding director of the MIT Institute for Data, Systems, and Society (IDSS).
Prof. Dahleh Leads a research group that focuses on Decisions Under Uncertainty. He is interested in Networked Systems, information design, and decision theory with applications to Social and Economic Networks, financial networks, Transportation Networks, Neural Networks, agriculture, and the Power Grid. He is also interested in causal learning for the purpose of intervention and control. His recent work focused on understanding the economics of data as well as deriving a foundational theory for data markets. He is a fellow of IEEE and IFAC.
