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Speaker Series: Quantitative Digital Humanities

2022-2023 Quantitative and Digital Humanities Speaker Series

AI Ethics Expanded: Interdisciplinary Approaches to Data, Computation, and Justice

Wednesday, March 15, 2023

Jessica Marie Johnson, Associate Professor of History, Johns Hopkins University

Kim Gallon, Associate Professor of Africana Studies, Brown University

Alexandre White, Assistant Professor of Sociology and the History of Medicine, Johns Hopkins School of Medicine 

Beyond Black Data: Community Ecosystems of Care

1:00-2:00 p.m., via Zoom (link TBA) 


Black Beyond Data is funded by the Andrew W. Mellon Foundation based at Johns Hopkins University, Brown University, and the Saint Francis Neighborhood center in Baltimore. Black Beyond Data's ultimate goals are to become a resource for Black digital humanities scholars, artists and community organizers focused on computational humanities and social justice. Our long-term vision is for Black Beyond Data to support a global shift in public knowledge about the ethics, methodologies, theories, and composition of Black data, and recenter Black communities in the stewardship of their past, present, and future data, broadly conceived.

3:00-4:00 p.m, via Zoom (link TBA)Informal Meeting with faculty and students

Co-sponsored by the Department of Quantitative Theory & Methods, the Department of English, the Department of African American Studies, the Department of Women’s, Gender, and Sexuality Studies, the James Weldon Johnson Institute, and the Hightower Fund. 

Wednesday, September 28, 2022

Wendy Chun, Canada 150 Research Chair in New Media, Simon Fraser University

Beyond Ethical Tech: Why Understanding the Sociocultural History of Our Technical Defaults Matter

4:00-5:30 p.m., Jones Room (311 Woodruff Library)


The dangers of predatory predictive algorithms are well known: from amplifying discrimination to cementing polarization. If this is so, what can we do? This talk outlines how the humanities, social sciences, and STEM might come together to address the problems we face not by ignoring the past but examining how past injustices, such as segregation, have been embedded within our technical defaults. 

Co-sponsored by the Department of Film and Media and the Hightower Fund

Contact: Dr. Lauren Klein (
Professor Chun


Dr. Chun will host an informal meeting with faculty and students on Wednesday, Sept 28, 1:00-2:00pm in PAIS 561.

Friday, October 21, 2022

Deen Freelon, Associate Professor, University of North Carolina Hussman School of Journalism and Media

Operation Dumpster Fire; or, toward balance in the detection and profiling of low-quality content online

1:00-2:30 p.m., PAIS 561


Mis- and disinformation, conspiracy theories, hyperpartisan distortions, and similar phenomena (collectively low-quality content) have grown into a major focus area for social science. Many of the quantitative studies in this area rely on blacklists of low-quality web domains—, and the like—to measure how much low-quality content exists and is viewed or shared on social media. While such studies have contributed much to our understanding of low-quality content, few of them empirically incorporate substantial amounts of high-quality content. Doing so may open new avenues for understanding low-quality content: for example, we could develop a taxonomy of misinformation attractors—individuals, places, institutions, and ideas that are frequent subjects of misinformation. We could also generate linguistic profiles of low-quality content, identifying specific words, phrases, and types of language that are statistically associated with it. Our research team is currently developing software, under the temporary designation Operation Dumpster Fire, to accomplish these and more research tasks related to low-quality content. This presentation will explore the project’s theoretical underpinnings, technical architecture, and possibly a feature demonstration (if development proceeds on schedule).


Co-sponsored by the Hightower Fund

Contact: Dr. Lauren Klein (
Professor Freelon

Monday, November 14, 2022

Aaron Roth, Henry Salvatori Professor of Computer and Cognitive Science, University of Pennsylvania

Robust and Equitable Uncertainty Estimation

Research seminar, 1:00-2:30 pm., Zoom: 

Machine learning provides us with an amazing set of tools to make predictions, but how much should we trust particular predictions? To answer this, we need a way of estimating the confidence we should have in particular predictions of black-box models. Standard tools for doing this give guarantees that are averages over predictions. For instance, in a medical application, such tools might paper over poor performance on one medically relevant demographic group if it is made up for by higher performance on another group. Standard methods also depend on the data distribution being static — in other words, the future should be like the past.

In this talk, I will describe new techniques to address both these problems: a way to produce prediction sets for arbitrary black-box prediction methods that have correct empirical coverage even when the data distribution might change in arbitrary, unanticipated ways and such that we have correct coverage even when we zoom in to focus on demographic groups that can be arbitrary and intersecting. When we just want correct group-wise coverage and are willing to assume that the future will look like the past, our algorithms are especially simple.

This talk is based on two papers, that are joint work with Osbert Bastani, Varun Gupta, Chris Jung, Georgy Noarov, and Ramya Ramalingam. 


The Ethical Algorithm

Open Lecture, 4:00-5:00 pm., Zoom: 

Techniques from machine learning and other forms of automated decision making are now being used in a wide variety of consequential domains, from finance to criminal justice. This is driven in part by an explosion in the amount of data that is constantly being gathered about individuals and the every-day actions they take interacting with the world. This has brought questions of privacy and fairness to the fore of the discussion on algorithmic decision making --- since we are using algorithms in roles that---were they handled by people---would come with expectations of respecting ethical norms like fairness and privacy. Translating these expectations into constraints that we place on mathematical models is fraught --- but very important. We will walk through two case studies in thinking about definitions mathematically --- in both data privacy and algorithmic fairness --- that will give a peak into current work on the topic.

This talk is based on the book "The Ethical Algorithm", authored jointly with Michael Kearns


Co-sponsored by the Hightower Fund

Contact: Dr. Lauren Klein (

Wednesday, March 15, 2023

Jessica Marie Johnson, Associate Professor of History, University of Maryland, College Park
Kim Gallon, Associate Professor of Africana Studies, Brown University
Alexandre White, Assistant Professor of Sociology and the History of Medicine, Johns Hopkins School of Medicine

1:00-2:00pm, via Zoom (link TBA)

Lecture — Black Beyond Data 
Abstract TBA

Co-sponsored by the James Weldon Johnson Institute 

Contact: Dr. Lauren Klein (
Professor Johnson:
Professor Gallon:
Professor White:


Drs. Johnson, Gallon, and White will host an informal meeting with faculty and students, 3:00-4:00 p.m, via Zoom (link TBA)