%0 Conference Paper %1 blodgett-etal-2020-language %A Blodgett, Su Lin %A Barocas, Solon %A Daumé III, Hal %A Wallach, Hanna %B Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics %C Online %D 2020 %I Association for Computational Linguistics %K bias language lm models nlp survey %P 5454--5476 %R 10.18653/v1/2020.acl-main.485 %T Language (Technology) is Power: A Critical Survey of ``Bias'' in NLP %U https://www.aclweb.org/anthology/2020.acl-main.485 %X We survey 146 papers analyzing ``bias'' in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing ``bias'' is an inherently normative process. We further find that these papers' proposed quantitative techniques for measuring or mitigating ``bias'' are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing ``bias'' in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of ``bias''---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements---and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.

@inproceedings{blodgett-etal-2020-language, abstract = {We survey 146 papers analyzing {``}bias{''} in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing {``}bias{''} is an inherently normative process. We further find that these papers{'} proposed quantitative techniques for measuring or mitigating {``}bias{''} are poorly matched to their motivations and do not engage with the relevant literature outside of NLP. Based on these findings, we describe the beginnings of a path forward by proposing three recommendations that should guide work analyzing {``}bias{''} in NLP systems. These recommendations rest on a greater recognition of the relationships between language and social hierarchies, encouraging researchers and practitioners to articulate their conceptualizations of {``}bias{''}---i.e., what kinds of system behaviors are harmful, in what ways, to whom, and why, as well as the normative reasoning underlying these statements{---}and to center work around the lived experiences of members of communities affected by NLP systems, while interrogating and reimagining the power relations between technologists and such communities.}, added-at = {2021-01-25T09:09:41.000+0100}, address = {Online}, author = {Blodgett, Su Lin and Barocas, Solon and Daum{\'e} III, Hal and Wallach, Hanna}, biburl = {https://www.bibsonomy.org/bibtex/219eac32b8c9e7cdad9cf9827e3f7ecd1/schwemmlein}, booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics}, description = {Language (Technology) is Power: A Critical Survey of “Bias” in NLP - ACL Anthology}, doi = {10.18653/v1/2020.acl-main.485}, interhash = {f6c0cc9c68e070af0f07f06ef23c4c48}, intrahash = {19eac32b8c9e7cdad9cf9827e3f7ecd1}, keywords = {bias language lm models nlp survey}, month = jul, pages = {5454--5476}, publisher = {Association for Computational Linguistics}, timestamp = {2021-01-25T09:09:41.000+0100}, title = {Language (Technology) is Power: A Critical Survey of {``}Bias{''} in {NLP}}, url = {https://www.aclweb.org/anthology/2020.acl-main.485}, year = 2020 }