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

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Manage episode 280193765 series 2789552
Content provided by Carnegie Mellon University. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.

  continue reading

41 episodes

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iconShare
 
Manage episode 280193765 series 2789552
Content provided by Carnegie Mellon University. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Carnegie Mellon University or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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.

  continue reading

41 episodes

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