"But not everyone believes gender bias should be eliminated from the data sets. Arvind Narayanan, an assistant professor of computer science at Princeton, has also analyzed word embedding and found gender, racial, and other prejudices. But Narayanan cautions against removing bias automatically, arguing that it could skew a computer’s representation of the real world and make it less adept at making predictions or analyzing data.
“We should think of these not as a bug but a feature,” Narayanan says. “It really depends on the application. What constitutes a terrible bias or prejudice in one application might actually end up being exactly the meaning you want to get out of the data in another application.”"
- Date Published:11/23/2016
- Original Publication:MIT Technology Review