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Databite No. 138: Poverty Lawgorithms | Michele Gilman and Meredith Broussard
Automated decision-making systems make decisions about our lives, and those with low socioeconomic status often bear the brunt of the harms these systems cause. “Poverty Lawgorithms: A Poverty Lawyers Guide to Fighting Automated Decision-Making
Harms on Low-Income Communities” is a guide by Data & Society Faculty Fellow
Michele Gilman to familiarize fellow poverty and civil legal services lawyers with the ins
and outs of data-centric and automated-decision making systems, so that they can
clearly understand the sources of the problems their clients are facing and effectively
advocate on their behalf.

Join Michele Gilman for a conversation with Professor Meredith Broussard on enhancing the digital literacy of poverty lawyers to better advocate the low-income communities they serve.

Oct 16, 2020 02:00 PM in Eastern Time (US and Canada)

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Speakers

Michele Gilman
Professor of Law @University of Baltimore School of Law
Michele Gilman is the Venable Professor of Law at the University of Baltimore School of Law. She directs the Saul Ewing Civil Advocacy Clinic, in which student attorneys represent individuals and community groups in a wide array of civil litigation and law reform projects. She also teaches Administrative Law and Evidence. In addition, Michele serves as a co-director of the Center on Applied Feminism, which works to apply the insights of feminist legal theory to legal practice and policy. Her recent scholarship focuses on the intersection of data privacy and poverty, with articles published in the California Law Review, Vanderbilt Law Review, and Washington University Law Review, among others, as well as in the media, including The Huffington Post, Salon, and The Conversation.
Meredith Broussard
Associate Professor, Data Journalist, Author @New York University
Meredith Broussard is a data journalist and associate professor at the Arthur L. Carter Journalism Institute of New York University and the author of “Artificial Unintelligence: How Computers Misunderstand the World.”. Her academic research focuses on artificial intelligence in investigative reporting, with a particular interest in using data analysis for social good. She is also interested in ethical AI and appeared in the 2020 documentary Coded Bias. She is an affiliate faculty member at the Moore Sloan Data Science Environment at the NYU Center for Data Science, a 2019 Reynolds Journalism Institute Fellow, and her work has been supported by the Institute of Museum & Library Services as well as the Tow Center at Columbia Journalism School. A former features editor at the Philadelphia Inquirer, she has also worked as a software developer at AT&T Bell Labs and the MIT Media Lab. Her features and essays have appeared in The Atlantic, Slate, and other outlets. Follow her on Twitter @merb