Abstract

Standing at this transformative moment for data science and artificial intelligence (AI), it is hard to predict the role that statistics and statisticians will play in the next ten years. In this ISI President’s invited address, I would like to argue that even in a world consumed with AI agents, the four foundational pillars of ISI (Ethics, Community, Communication and Capacity Building) are critical to our profession, to our individual careers, and to our collective survival. I will lean on my own experience as a public health statistician to place examples in each of these four quadrants.

Ethics: AI algorithms and systems developed on exclusionary datasets and biased training corpora can lead to erroneous conclusions and misguided policies, furthering disparities. Statisticians can play a pivotal role in mitigating systematic sources of bias — an expertise that few other quantitative disciplines possess. By addressing selection, information and confounding bias, we can contribute to enhancement of ethics and equity in data science and AI. I will share my recent work on data equity to make this point. Community: I urge statisticians to lead efforts for creating, curating, collecting new and better data and pioneering scientific studies, not just remain on the design and analytic fringes. As a public health statistician, my job is not just to predict efficiently, but to prevent effectively by engaging in bi-directional research collaboration with communities near and far. I will share my experiences of building community partnerships in statistics and launching field studies. Communication: The transformative experience of modeling COVID-19 in India and engaging extensively with global media platforms taught me valuable lessons in scientific communications. It is important that we claim our lane and be present in the public eye as a primary and essential form of science. Finally, Capacity Building is the only thing that outlives our finite careers. I will share my experience of running a summer health data science program for more than a decade in the United States and creating an adapted version of the program for early career researchers in India. I will present data to illustrate the ripple effect and impact of these programs. I hope there will be something for everyone in at least one of the four quadrants of the lecture.