@inproceedings{koolen-van-cranenburgh-2017-stereotypes,
title = "These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution",
author = "Koolen, Corina and
van Cranenburgh, Andreas",
editor = "Hovy, Dirk and
Spruit, Shannon and
Mitchell, Margaret and
Bender, Emily M. and
Strube, Michael and
Wallach, Hanna",
booktitle = "Proceedings of the First {ACL} Workshop on Ethics in Natural Language Processing",
month = apr,
year = "2017",
address = "Valencia, Spain",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-1602",
doi = "10.18653/v1/W17-1602",
pages = "12--22",
abstract = "Stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy. However, it is difficult to explain what gives rise to these results and there are many possible confounding factors, such as the domain, genre, and target audience of a text. More fundamentally, such classification efforts risk invoking stereotyping and essentialism. We explore this issue in two datasets of Dutch literary novels, using commonly used descriptive (LIWC, topic modeling) and predictive (machine learning) methods. Our results show the importance of controlling for variables in the corpus and we argue for taking care not to overgeneralize from the results.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="koolen-van-cranenburgh-2017-stereotypes">
<titleInfo>
<title>These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution</title>
</titleInfo>
<name type="personal">
<namePart type="given">Corina</namePart>
<namePart type="family">Koolen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Andreas</namePart>
<namePart type="family">van Cranenburgh</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2017-04</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the First ACL Workshop on Ethics in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Dirk</namePart>
<namePart type="family">Hovy</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shannon</namePart>
<namePart type="family">Spruit</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Margaret</namePart>
<namePart type="family">Mitchell</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Emily</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Bender</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Michael</namePart>
<namePart type="family">Strube</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Hanna</namePart>
<namePart type="family">Wallach</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Valencia, Spain</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>Stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy. However, it is difficult to explain what gives rise to these results and there are many possible confounding factors, such as the domain, genre, and target audience of a text. More fundamentally, such classification efforts risk invoking stereotyping and essentialism. We explore this issue in two datasets of Dutch literary novels, using commonly used descriptive (LIWC, topic modeling) and predictive (machine learning) methods. Our results show the importance of controlling for variables in the corpus and we argue for taking care not to overgeneralize from the results.</abstract>
<identifier type="citekey">koolen-van-cranenburgh-2017-stereotypes</identifier>
<identifier type="doi">10.18653/v1/W17-1602</identifier>
<location>
<url>https://aclanthology.org/W17-1602</url>
</location>
<part>
<date>2017-04</date>
<extent unit="page">
<start>12</start>
<end>22</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution
%A Koolen, Corina
%A van Cranenburgh, Andreas
%Y Hovy, Dirk
%Y Spruit, Shannon
%Y Mitchell, Margaret
%Y Bender, Emily M.
%Y Strube, Michael
%Y Wallach, Hanna
%S Proceedings of the First ACL Workshop on Ethics in Natural Language Processing
%D 2017
%8 April
%I Association for Computational Linguistics
%C Valencia, Spain
%F koolen-van-cranenburgh-2017-stereotypes
%X Stylometric and text categorization results show that author gender can be discerned in texts with relatively high accuracy. However, it is difficult to explain what gives rise to these results and there are many possible confounding factors, such as the domain, genre, and target audience of a text. More fundamentally, such classification efforts risk invoking stereotyping and essentialism. We explore this issue in two datasets of Dutch literary novels, using commonly used descriptive (LIWC, topic modeling) and predictive (machine learning) methods. Our results show the importance of controlling for variables in the corpus and we argue for taking care not to overgeneralize from the results.
%R 10.18653/v1/W17-1602
%U https://aclanthology.org/W17-1602
%U https://doi.org/10.18653/v1/W17-1602
%P 12-22
Markdown (Informal)
[These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution](https://aclanthology.org/W17-1602) (Koolen & van Cranenburgh, EthNLP 2017)
ACL