Enron, Wikipedia and the Deal with Biased Low-Friction Data
Consequential
English - December 16, 2020 05:01 - 29 minutes - 26.9 MB - ★★★★★ - 29 ratingsTechnology Government aiforgood futureofwork publicpolicy techpolicy aiforsocialgood artificialintelligence blockcenter carnegiemellonuniversity cmu dataforgood Homepage Download Apple Podcasts Google Podcasts Overcast Castro Pocket Casts RSS feed
Previous Episode: Can automation make peer review faster and fairer?
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.