Enron, Wikipedia and the Deal with Biased Low-Friction Data

The Enron emails helped give us spam filters, and many natural language processing and fact-checking algorithms rely on data from Wikipedia. While these data resources are plentiful and easily accessible, they are also highly biased. This week, we speak to guests Amanda Levendowski and Katie Willingham about how low-friction data sources contribute to algorithmic bias and the role of copyright law in accessing less troublesome sources of knowledge and data.

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Consequential is a narrative podcast about public policy, its impacts, and its potential for building a better future. The show is produced by Carnegie Mellon University's Heinz College of Information Systems and Public Policy. https://hnz.cm/consequential