NSF Awards Nearly $3 Million For Graduate Research Training On Detecting And Addressing Bias In Data, Humans, And Institutions


Data science and artificial intelligence (AI) have shown the capability to generate new knowledge, fuel innovation, and deal with some of society's most pressing problems. However, biases inherent in humans and institutions can be perpetuated and magnified by "big data" and machine learning tools.  Now, a new National Science Foundation Research Traineeship (NRT) award of nearly $3 million will enable Stony Brook University to provide interdisciplinary cross-training to PhD students in the computational and data sciences alongside PhD students in the human-centered sciences to detect and address biases in data, models, people, and institutions.

This cross-disciplinary project is an important step towards training well-rounded researchers who are as fluent in computational and data sciences as in human-centered sciences.  From a  Computer Science perspective, it is important to nurture a cohort of researchers who recognize that the larger context of data -- how the data collection is managed, who designs the processes, how its impact is measured -- is as important as its content.

 This project is led by PI Susan Brennan (Psychology), and is a collaboration between Computer Science (C. R. Ramakrishnan, Niranjan Balasubramanian, Klaus Mueller, Andrew Schwartz and Steven Skiena) and other departments of Applied Math and Statistics, and Electrical and Computer Engineering in the College of Engineering and Applied Sciences, and several human-centered STEM departments in the College of Arts and Sciences. 

Also, see the campus media release on this project. 

Back: Memming Park, Susan Brennan, Jeffrey Heinz, Adryan Wallace, CR Ramakrishnan, Reuben Kline.

Front: Wei Zhu, Bonita London, Owen Rambow