Papers presented by Louis Hyman since 2019

2024 Providence, Rhode Island

"Big Labor/Big Data: AI Approaches to the History of the American Federation of Labor "

Abstract:

During the late 19th century, the American Federation of Labor, led by Samuel Gompers, fought its way to the core of the United States’ rapidly evolving political economy. Starting from a position of limited institutional power in relation to both its constituent unions and the broader world of business and politics, its rise was, to a remarkable extent, a triumph of communication. Over a period of 30-odd years, Gompers wrote—a torrent of correspondence to union leaders, activists, politicians, business-owners, journalists, and workers. Through these letters he directed strategy, created connections, exhorted action, adjusted jurisdictions, and settled (or initiated) bureaucratic infighting. In doing so, he became among the most powerful figures in the Progressive era, exerting enormous influence on the evolution of American labor—and the shape of American capitalism. The very methods Gompers used to cement his power make this legacy difficult to explore. Simply put, there is too much of it. The Library of Congress holds over 300,000 pages of his correspondence, with related materials pushing the count to over a million. A multi-decade project to edit these collections could only scratch the surface. Computational methods offer a potential solution. Working with the Johns Hopkins Center for Digital Humanities, we built a workflow to OCR and analyze the library’s materials. Leveraging recent advancements in AI, we devised techniques to extract unstructured information, such as dates, named entities, correspondence relationships, and sentiment. Recovering broad patterns within the archive allows us to look past a handful of strikes or industrial locations, exploring the cumulative impact of a more widespread set of relationships, businesses, leaders, and organizations. These methods make it possible to develop a newly expansive vision of this key organization, shedding light on the institutional successes—and failures—that lay the foundations for the relationship between labor and capital in the 20th century.

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2024 Providence, Rhode Island

"Teaching AI and History"

Abstract:

During the past year, we have lived through a revolution in the accessibility and power of large-language models. This fall at Cornell, I decided to teach a class on that required the use of ChatGPT for all students. The course combined a traditional econometrics sequence with readings in critical data practices in history. Students, who had never coded before, used real historical data sets (ranging from the 1850 Census to the 1950 Survey of Consumer Finance) in ever-more complicated historical analyses. AI was used as an accelerant to give students the tools to quickly ask larger historical questions and probe more complicated data sets. By using AI, students could focus on historical methods and approaches while still using data. I will describe the class and its methods, its successes and failures, as one model of how to incorporate AI into the study of history. I remain very optimistic about its ability to draw students into historical thinking (and the history major!) while giving them tools (statistics, python, AI) that will be useful in the world.

2023 Detroit, MI, United States

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