Just Machine Learning
Machine learning algorithms are affecting every aspect of our lives, from the mundane, such as movie recommendation, to the life-altering, such as recidivism assessment. Arthur Samuel coined the term “machine learning” in 1959. He called it “the field of study that gives computers the ability to learn without being explicitly programmed.” A commonly used machine learning algorithm is your email spam filter. It sorts your email messages into two bins, spam and not spam, based on observing which emails you put in the junk folder. Many of the machine learning algorithms that are currently being used in high-stakes decisions—such as criminal justice, policing, hiring, loan approvals and school assignments—do a similar kind of sorting. For example, Jack’s risk of defaulting on a loan is high, Jill’s is not; Ed’s risk of recidivism is high, Peter’s is not. GRI Faculty Affiliate and Professor of Computer Science Tina Eliassi-Rad will discuss the good, the bad, and the ugly when it comes to the usage of machine learning in our society; and the reasons behind them. Professor Eliass-Rad will finish with a thought experiment: Would you want a machine learning algorithm to make a life-altering decision about you? If so, why? If not, why not?
About the Speaker:
Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University in Boston, MA. She is also a core faculty member at Northeastern’s Network Science Institute. 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 data mining, machine learning, and network science. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awardees); and has given over 200 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, and ethics in machine learning. Her algorithms have been incorporated into systems used by the government and industry (e.g., IBM System G Graph Analytics) as well as open-source software (e.g., Stanford Network Analysis Project).
The event will take place, Wednesday, May 26 at 12:00 to 1:00 PM ET. To register, click here.