DSI | Just machine learning
Just machine learning
In this talk, I will address some concerns about the use of machine learning in situations where the stakes are high (such as criminal justice, law enforcement, employment decisions, credit scoring, health care, public eligibility assessment, and school assignments). First, I will discuss the popular task of risk assessment and impossibility results for group fairness, where one cannot simultaneously satisfy desirable probabilistic measures of fairness. Second, I will present how machine learning can be used to generate aspirational data (i.e., data that are free of biases present in real data). Such data are useful for recognizing sources of unfairness in machine learning models besides biased data. Third, I will describe how information access equality in complex networks is an interplay between the network structure and the spreading process, leading to a tradeoff between equality and efficiency in certain circumstances. If time permits, I will discuss the steps needed to measure our algorithmically infused societies and present our findings from a 2022 qualitative study examining responsibility and deliberation in AI impact statements and ethics reviews.
Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern's Network Science Institute and the Institute for Experiential AI. In addition, she is an external faculty member at the Santa Fe Institute and the Vermont Complex Systems Center. Prior to joining Northeastern, Tina was an Associate Professor of Computer Science at Rutgers University; and before that she was a Member of Technical Staff and Principal Investigator at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison. Her research is at the intersection of data mining, machine learning, and network science. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awards); and has given over 250 invited talks and 14 tutorials. Tina's work has been applied to personalized search on the World-Wide Web, statistical indices of large-scale scientific simulation data, fraud detection, mobile ad targeting, cyber situational awareness, drug discovery, democracy and online discourse, and ethics in machine learning. Her algorithms have been incorporated into systems used by governments and industry (e.g., IBM System G Graph Analytics), as well as open-source software (e.g., Stanford Network Analysis Project). In 2017, Tina served as the program co-chair for the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (a.k.a. KDD, which is the premier conference on data mining) and as the program co-chair for the International Conference on Network Science (a.k.a. NetSci, which is the premier conference on network science). In 2020, she served as the program co-chair for the International Conference on Computational Social Science (a.k.a. IC2S2, which is the premier conference on computational social science). Tina received an Outstanding Mentor Award from the U.S. Department of Energy's Office of Science in 2010, became an ISI Foundation Fellow in 2019, was named one of the 100 Brilliant Women in AI Ethics in 2021, and received Northeastern University's Excellence in Research and Creative Activity Award in 2022.
LLNL-VIDEO-839394