Divyansh Kaushik will be giving a virtual talk on Friday, April 8, from 11am-noon PST.   Zoom information will be distributed via the Berkeley NLP Seminar listserv for those wishing to attend remotely.

Title: Robustifying NLP with Humans in the Loop

Abstract: Most machine learning methods address prediction problems under restrictive assumptions but when applied to drive decisions in environments where those assumptions are violated. This disconnect between what the methodological framework offers and the desired applications have caused confusion both among researchers (who often lack the right formalism to tackle these problems coherently), practitioners (who have developed a folks tradition of ad hoc practices for deploying and monitoring systems), and regulators (who have applied frameworks designed for biomedical ethics to machine learning). In this talk I’ll discuss some of these issues affecting the application of machine learning and our fledgling efforts to bridge some of these gaps by injecting causal knowledge via humans in the loop, along with some critical disconnects between how humans are employed in ML research to perform various tasks and the regulatory framework around research ethics, and its implications.

Bio: Divyansh Kaushik is a PhD Candidate at the Language Technologies Institute in the School of Computer Science at Carnegie Mellon University, and a Science and Technology Policy Fellow at the Federation of American Scientists. He is advised by Dr. Eduard Hovy and Dr. Zachary Lipton and in the Approximately Correct Machine Intelligence (ACMI) Lab. An Amazon Graduate Research Fellow, Divyansh’s interests lie in exploring human-AI interaction. Over the years, his work has been supported by Amazon AI, Pricewaterhouse Coopers, and Facebook AI. He is also the President of CMU’s Graduate Student Assembly and has written on several science policy issues (recently appearing in Forbes, Institute for Progress, Issues in Science and Technology and PublicSource).