These are not the Stereotypes You are Looking For: Bias and
            Fairness in Authorial Gender Attribution

Koolen, Corina; Cranenburgh, Andreas van

doi:10.18653/v1/w17-1602

Cited by 38 publications

(28 citation statements)

References 27 publications

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“…Tatman (2017) investigates the impact of gender and dialect on deployed speech recognition systems, while introduce a method to reduce amplification effects on models trained with gender-biased datasets. Koolen and van Cranenburgh (2017) examine the relationship between author gender and text attributes, noting the potential for researcher interpretation bias in such studies. Both Larson (2017) and Koolen and van Cranenburgh (2017) offer guidelines to NLP researchers and computational social scientists who wish to predict gender as a variable.…”

Section: Related Workmentioning

confidence: 99%

Gender Bias in Coreference Resolution

Rudinger¹,

Naradowsky²,

Leonard³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

338

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We present an empirical study of gender bias in coreference resolution systems. We first introduce a novel, Winograd schema-style set of minimal pair sentences that differ only by pronoun gender. With these Winogender schemas, we evaluate and confirm systematic gender bias in three publicly-available coreference resolution systems, and correlate this bias with real-world and textual gender statistics.

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Section: Related Workmentioning

confidence: 99%

Gender Bias in Coreference Resolution

Rudinger¹,

Naradowsky²,

Leonard³

et al. 2018

Proceedings of the 2018 Conference of the North American Chapter Of the Association for Computational Linguistics: Hu

338

View full text Add to dashboard Cite

show abstract

“…Human judges show surprisingly inferior performance on user profiling tasks, grounding their judgement in topical stereotypes (Carpenter et al, 2017). However, albeit more accurate thanks to capturing stylistic variation elements, statistical models are prone to stereotype propagation as well (Costa-jussà et al, 2019;Koolen and van Cranenburgh, 2017).…”

Section: User Traits and Nlp Modelsmentioning

confidence: 99%

Returning the N to NLP: Towards Contextually Personalized Classification Models

Flek¹

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

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Most NLP models today treat language as universal, even though socio-and psycholingustic research shows that the communicated message is influenced by the characteristics of the speaker as well as the target audience. This paper surveys the landscape of personalization in natural language processing and related fields, and offers a path forward to mitigate the decades of deviation of the NLP tools from sociolingustic findings, allowing to flexibly process the "natural" language of each user rather than enforcing a uniform NLP treatment. It outlines a possible direction to incorporate these aspects into neural NLP models by means of socially contextual personalization, and proposes to shift the focus of our evaluation strategies accordingly.

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“…For example, gender is usually represented as a binary variable in NLP; computational models built on this foundation risk learning gender-stereotypical patterns. For this reason, a growing line of research has sought new ways to operationalize gender in NLP (Bamman et al, 2014a ; Nguyen et al, 2014 ; Koolen and van Cranenburgh, 2017 ).…”

Section: Operationalizationmentioning

confidence: 99%

How We Do Things With Words: Analyzing Text as Social and Cultural Data

Nguyen

Liakata

DeDeo

et al. 2020

Front. Artif. Intell.

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In this article we describe our experiences with computational text analysis involving rich social and cultural concepts. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of key questions that can guide work in this area. Our guidance is based on our own experiences and is therefore inherently imperfect. Still, given our diversity of disciplinary backgrounds and research practices, we hope to capture a range of ideas and identify commonalities that resonate for many. This leads to our final goal: to help promote interdisciplinary collaborations. Interdisciplinary insights and partnerships are essential for realizing the full potential of any computational text analysis involving social and cultural concepts, and the more we bridge these divides, the more fruitful we believe our work will be.

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These are not the Stereotypes You are Looking For: Bias and Fairness in Authorial Gender Attribution

Cited by 38 publications

References 27 publications

Gender Bias in Coreference Resolution

Gender Bias in Coreference Resolution

Returning the N to NLP: Towards Contextually Personalized Classification Models

How We Do Things With Words: Analyzing Text as Social and Cultural Data

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