Seminar Series Archive

Recent advances in deep neural networks have revolutionized many natural language processing applications. These approaches automatically learn how to make decisions based on the statistics and diagnostic information from large amounts of training data. Despite the remarkable accuracy of machine learning in various applications, learning algorithms run the risk of relying on societal biases encoded in the training data to make predictions. Therefore, machine learning algorithms risk potentially encouraging unfair and discriminatory decision making and raise serious privacy concerns. Without properly quantifying and reducing the reliance on such correlations, the broad adoption of these models might have the undesirable effect of magnifying harmful stereotypes or implicit biases that rely on sensitive demographic attributes. In this talk, I will discuss a collection of results that quantify and control implicit societal biases in a wide spectrum of vision and language tasks, including word embeddings, coreference resolution, and visual semantic role labeling. These results lead to greater control of NLP systems to be socially responsible and accountable.

Kai-Wei Chang is an assistant professor in the Department of Computer Science at the University of California Los Angeles. His research interests include designing robust machine learning methods for large and complex data and building fair and accountable language processing approaches for social good applications. Kai-Wei has published broadly in machine learning, natural language processing, and artificial intelligence. His awards include the EMNLP Best Long Paper Award (2017), the KDD Best Paper Award (2010), and the Okawa Research Grant Award (2018). Additional information is available at http://kwchang.net.