INvited speakers

The public is increasingly concerned about computing's negative societal impacts, e.g. "algorithmic bias", computing-induced wealth inequality, and online misinformation. In this talk, I will discuss how computational social science can be used to illuminate and mitigate these and other issues in the "social science of computing". To do so, I will cover several projects from my lab that enhanced our understanding of computing's negative societal impacts and have shed light on how we might address them. These projects include our early work that helped to characterize what is now known as "algorithmic bias" and our recent work that seeks to use computational tools to reduce computing-induced wealth inequality. I will close this interactive presentation by highlighting other exciting research directions that “turn the mirror around” and use the perspectives and methods of computational social science to advance the social science of computing.

Bio: Dr. Brent Hecht is an assistant professor at Northwestern University, where he directs the People, Space and Algorithms (PSA) Research Group. The mission of the PSA Research Group is to “identify and address societal problems that are created or exacerbated by advances in computer science.” Dr. Hecht is particularly interested in understanding and mitigating the cultural, geographic, and economic biases that are reflected and reinforced by artificial intelligence systems and other computing technologies.

Dr. Hecht received a Ph.D. in computer science from Northwestern University, a Master’s degree in geography from UC Santa Barbara, and dual Bachelor’s degrees in computer science and geography from Macalester College. He is the recipient of a CAREER award from the U.S. National Science Foundation and has received awards for his research at top-tier publication venues in human-computer interaction, data science, and geography (e.g. ACM SIGCHI, ACM CSCW, ACM Mobile HCI, AAAI ICWSM, COSIT). Dr. Hecht serves on the Executive Committee of ACM FAT* (www.fatconference.org), the premier publication venue for research on understanding and mitigating societal biases in artificial intelligence systems. Dr. Hecht has also collaborated with Google Research, Xerox PARC, and Microsoft Research, and his work has been featured by The New York Times, the Washington Post, Le Monde, Der Spiegel, and various other TV, radio, and Internet outlets.

Language is not only about the words, it is also about the people. While much of the work in computational linguistics has focused almost exclusively on words (and their relations), recent research in the emerging field of computational sociolinguistics has shown that we can effectively leverage the close interplay between language and people. In this talk, I will explore this interaction, and show (1) that we can develop cross-cultural language models to identify words that are used in significantly different ways by speakers from different cultures; and (2) that we can effectively use information about the people behind the words to build better language models. This is joint work with Jamie Pennebaker, Aparna Garimella, Carmen Banea.

Bio: Rada Mihalcea is a Professor of Computer Science and Engineering at the University of Michigan and the Director of the Michigan Artificial Intelligence Lab. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the Journals of Computational Linguistics, Language Resources and Evaluations, Natural Language Engineering, Journal of Artificial Intelligence Research, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a program co-chair for EMNLP 2009 and ACL 2011, and a general chair for NAACL 2015 and *SEM 2019. She currently serves as the ACL Vice-President Elect. She is the recipient of a National Science Foundation CAREER award (2008) and a Presidential Early Career Award for Scientists and Engineers awarded by President Obama (2009). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.

Patients and caregivers are generating troves of unstructured text content pertaining to their health journeys. For example, one online health support community, CaringBridge.org, is host to over 700,000 sites where patients and caregivers post regularly about their experiences with conditions like cancer, stroke, organ transplants, and childbirth. The ready availability and volume of this user-generated data support new forms of quantitative analysis, potentially providing a treasure trove of evidence and a new resource for patient-centered healthcare. However, as a community, we may want to explicitly consider the limitations of such data sources and our responsibility to reflect on potential harms of re-purposing user-generated data.

Bio: Svetlana “Lana” Yarosh is an Assistant Professor in the Computer Science & Engineering Department at University of Minnesota. Her research in HCI focuses on embodied interaction in social computing systems. Lana is currently most proud of getting both the NSF CRII and the NSF CAREER awards, of her best papers at CHI 2013 and CSWC 2014, and of receiving the McKnight Land Grant Professorship. Lana has two Bachelors of Science from University of Maryland (in Computer Science and Psychology), a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, and two years of industry research experience with AT&T Labs Research.

Linguistic properties of dialects of American English, such as African American English (AAE), are general topics in sociolinguistic research that focuses on contexts of language variation and linguistic and extralinguistic factors contributing to variation. In fact, some of the textbook case studies that are used to illustrate the relationship between social factors and language use reflect variable properties in AAE, such as the variability in production of the copula and auxiliary be(e.g., Mike runningv. Mike’srunning), on the one hand, and itsobligatory occurrence in the environments of 1st person singular (I’m) and 3rd person singular neuter (it’s), on the other. Theoverwhelming majority of research on variation in AAE has been from the perspective of variable rules and related analyses in which frequency and probability of occurrence of variable morphosyntactic forms are considered in relation to social and linguistic conditioning; however, descriptions and analyses of the structure of the AAE grammar are also cast in formal and theoretical frameworks. Current proposals in syntactic theory have been used in analyses of tense/aspect and negation systems of the linguistic variety and shed light on subtle and obvious structural differences between corresponding systems in AAE and other varieties of English and among regional varieties of AAE. Although AAE is the most commonly studied variety of American English, an incredibly limited amount of computational research that could provide insights to subtle differences between AAE and other dialects of English has been conducted on the variety. This presentation addresses the state of research on AAE from the angle of corpora and large datasets and explains the theoretical implications and broader impacts of approaches to the study of AAE from the perspective of computational analysis and natural language processing.

Bio: Lisa Green holds a PhD in Linguistics from the University of Massachusetts Amherst. She returned to UMass after eleven years in the Department of Linguistics at the University of Texas at Austin. Green is the founding director of the Center for the Study of African American Language at UMass Amherst. Green’s research investigates variation within and across varieties of English, with a focus on African American English (AAE). In her undergraduate teaching and workshops on dialects, Green considers ways of integrating linguistic description and practical application and conveying such strategies to diverse audiences.