Stereotypes in High Stakes Decisions: Evidence from U.S. Circuit Courts

Chen, Daniel L.; Ash, Elliott; Ornaghi, Arianna

doi:10.2139/ssrn.3749842

Cited by 5 publications

(2 citation statements)

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“…Estimating these conditional differences is a challenging statistical problem due to the non-standard nature of text data, and researchers are faced with two key decisions when operationalizing an estimator using text data: how to encode the text data into numerical features, and how to select a suitable learner given the encoded data. Both decisions are ex-ante challenging, but also practically highly relevant as text data is becoming increasingly encountered in economic applications (e.g., Gentzkow and Shapiro, 2010;Chen and Ornaghi, 2023;Widmer, Galletta, and Ash, 2023). We show that these decisions can be consequential and that by simultaneously considering different encoding procedures and multiple learners,…”

Section: Gender Gap In Citationsmentioning

confidence: 99%

“…First, we examine gender gaps in citations of articles published in top-30 economic journals from 1983 to 2020, and assess how the difference in citations change when conditioning on content and quality proxied by the abstract text. Estimating these conditional differences is a challenging statistical problem due to the non-standard nature of text data, which is increasingly encountered in economic applications (see also e.g., Ash and Hansen, 2023;Chen and Ornaghi, 2023;Eberhardt, Facchini, and Rueda, 2022). Second, we revisit a UK sample of the OECD Skill Survey to estimate semiparametric Kitagawa-Oxaca-Binder estimates of the unexplained gender wage gap.…”

Section: Introductionmentioning

confidence: 99%

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ddml: Double/Debiased Machine Learning in Stata

et al. 2023

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Section: Gender Gap In Citationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

ddml: Double/Debiased Machine Learning in Stata

et al. 2023

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Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks

et al. 2020

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Cutting-edge data science techniques can shed new light on fundamental questions in educational research. We apply techniques from natural language processing (lexicons, word embeddings, topic models) to 15 U.S. history textbooks widely used in Texas between 2015 and 2017, studying their depiction of historically marginalized groups. We find that Latinx people are rarely discussed, and the most common famous figures are nearly all White men. Lexicon-based approaches show that Black people are described as performing actions associated with low agency and power. Word embeddings reveal that women tend to be discussed in the contexts of work and the home. Topic modeling highlights the higher prominence of political topics compared with social ones. We also find that more conservative counties tend to purchase textbooks with less representation of women and Black people. Building on a rich tradition of textbook analysis, we release our computational toolkit to support new research directions.

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Sexism in the Judiciary: The Importance of Bias Definition in NLP and In Our Courts

Gillis¹

2021

Proceedings of the 3rd Workshop on Gender Bias in Natural Language Processing

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We analyze 6.7 million case law documents to determine the presence of gender bias within our judicial system. We find that current bias detection methods in NLP are insufficient to determine gender bias in our case law database and propose an alternative approach. We show that existing algorithms' inconsistent results are consequences of prior research's inconsistent definitions of biases themselves. Bias detection algorithms rely on groups of words to represent bias (e.g., 'salary,' 'job,' and 'boss' to represent employment as a potentially biased theme against women in text). However, the methods to build these groups of words have several weaknesses, primarily that the word lists are based on the researchers' own intuitions. We suggest two new methods of automating the creation of word lists to represent biases. We find that our methods outperform current NLP bias detection methods. Our research improves the capabilities of NLP technology to detect bias and highlights gender biases present in influential case law. In order to test our NLP bias detection method's performance, we regress our results of bias in case law against U.S census data of women's participation in the workforce in the last 100 years.

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Stereotypes in High Stakes Decisions: Evidence from U.S. Circuit Courts

Cited by 5 publications

References 0 publications

ddml: Double/Debiased Machine Learning in Stata

ddml: Double/Debiased Machine Learning in Stata

Content Analysis of Textbooks via Natural Language Processing: Findings on Gender, Race, and Ethnicity in Texas U.S. History Textbooks

Sexism in the Judiciary: The Importance of Bias Definition in NLP and In Our Courts

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