Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification.
Daniel Borkan|Lucas Dixon|Jeffrey Sorensen|Nithum Thain|Lucy Vasserman
| Anthology ID: | DBLP:conf/www/BorkanDSTV19 |
|---|---|
| Volume: | Companion of The 2019 World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019. |
| Year: | 2019 |
| Venue: | The Web Conference (WWW) |
| Publisher: | ACM |
| Pages: | 491-500 |
| URL: | https://doi.org/10.1145/3308560.3317593 |
| DOI: | https://doi.org/10.1145/3308560.3317593 |
| DBLP: | conf/www/BorkanDSTV19 |
| BibTeX: |
@inproceedings{borkan-2019-nuanced,
author = {Daniel Borkan and
Lucas Dixon and
Jeffrey Sorensen and
Nithum Thain and
Lucy Vasserman},
editor = {Sihem Amer-Yahia and
Mohammad Mahdian and
Ashish Goel and
Geert-Jan Houben and
Kristina Lerman and
Julian J. McAuley and
Ricardo Baeza-Yates and
Leila Zia},
title = {{Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification}},
booktitle = {{Companion of The 2019 World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019}},
pages = {491--500},
publisher = {ACM},
year = {2019},
url = {https://doi.org/10.1145/3308560.3317593},
doi = {https://doi.org/10.1145/3308560.3317593}
}