Gender Bias in Resident Evaluations: Natural Language Processing and Competency Evaluation

2021 ◽  
Author(s):  
Jane Andrews ◽  
David Chartash ◽  
Seonaid Hay
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Gender Bias ◽  
Language Processing ◽  
Competency Evaluation

scholarly journals Gender Bias in Natural Language Processing Across Human Languages

2021 ◽  
Author(s):  
Abigail Matthews ◽  
Isabella Grasso ◽  
Christopher Mahoney ◽  
Yan Chen ◽  
Esma Wali ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Gender Bias ◽  
Language Processing

scholarly journals Mitigating Gender Bias in Natural Language Processing: Literature Review

2019 ◽  
Author(s):  
Tony Sun ◽  
Andrew Gaut ◽  
Shirlyn Tang ◽  
Yuxin Huang ◽  
Mai ElSherief ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Literature Review ◽  
Natural Language ◽  
Gender Bias ◽  
Language Processing

scholarly journals The Gender Gap Tracker: Using Natural Language Processing to measure gender bias in media

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245533
Author(s):  
Fatemeh Torabi Asr ◽  
Mohammad Mazraeh ◽  
Alexandre Lopes ◽  
Vasundhara Gautam ◽  
Junette Gonzales ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Gender Bias ◽  
Goal Setting ◽  
Language Processing ◽  
Gender Gap ◽  
English Language ◽  
General Pattern ◽  
Time Intervals ◽  
News Organizations

We examine gender bias in media by tallying the number of men and women quoted in news text, using the Gender Gap Tracker, a software system we developed specifically for this purpose. The Gender Gap Tracker downloads and analyzes the online daily publication of seven English-language Canadian news outlets and enhances the data with multiple layers of linguistic information. We describe the Natural Language Processing technology behind this system, the curation of off-the-shelf tools and resources that we used to build it, and the parts that we developed. We evaluate the system in each language processing task and report errors using real-world examples. Finally, by applying the Tracker to the data, we provide valuable insights about the proportion of people mentioned and quoted, by gender, news organization, and author gender. Data collected between October 1, 2018 and September 30, 2020 shows that, in general, men are quoted about three times as frequently as women. While this proportion varies across news outlets and time intervals, the general pattern is consistent. We believe that, in a world with about 50% women, this should not be the case. Although journalists naturally need to quote newsmakers who are men, they also have a certain amount of control over who they approach as sources. The Gender Gap Tracker relies on the same principles as fitness or goal-setting trackers: By quantifying and measuring regular progress, we hope to motivate news organizations to provide a more diverse set of voices in their reporting.

scholarly journals Quantification of Gender Bias and Sentiment Toward Political Leaders Over 20 Years of Kenyan News Using Natural Language Processing

2021 ◽  
Vol 12 ◽  
Author(s):  
Emma Pair ◽  
Nikitha Vicas ◽  
Ann M. Weber ◽  
Valerie Meausoone ◽  
James Zou ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Sentiment Analysis ◽  
Gender Equality ◽  
Gender Bias ◽  
Language Processing ◽  
Temporal Trends ◽  
Constitutional Amendment ◽  
Word Embeddings ◽  
Political Leaders

Background: Despite a 2010 Kenyan constitutional amendment limiting members of elected public bodies to < two-thirds of the same gender, only 22 percent of the 12th Parliament members inaugurated in 2017 were women. Investigating gender bias in the media is a useful tool for understanding socio-cultural barriers to implementing legislation for gender equality. Natural language processing (NLP) methods, such as word embedding and sentiment analysis, can efficiently quantify media biases at a scope previously unavailable in the social sciences.Methods: We trained GloVe and word2vec word embeddings on text from 1998 to 2019 from Kenya’s Daily Nation newspaper. We measured gender bias in these embeddings and used sentiment analysis to predict quantitative sentiment scores for sentences surrounding female leader names compared to male leader names.Results: Bias in leadership words for men and women measured from Daily Nation word embeddings corresponded to temporal trends in men and women’s participation in political leadership (i.e., parliamentary seats) using GloVe (correlation 0.8936, p = 0.0067, r2 = 0.799) and word2vec (correlation 0.844, p = 0.0169, r2 = 0.712) algorithms. Women continue to be associated with domestic terms while men continue to be associated with influence terms, for both regular gender words and female and male political leaders’ names. Male words (e.g., he, him, man) were mentioned 1.84 million more times than female words from 1998 to 2019. Sentiment analysis showed an increase in relative negative sentiment associated with female leaders (p = 0.0152) and an increase in positive sentiment associated with male leaders over time (p = 0.0216).Conclusion: Natural language processing is a powerful method for gaining insights into and quantifying trends in gender biases and sentiment in news media. We found evidence of improvement in gender equality but also a backlash from increased female representation in high-level governmental leadership.

scholarly journals Gender Bias in Neural Natural Language Processing

2020 ◽  
pp. 189-202
Author(s):  
Kaiji Lu ◽  
Piotr Mardziel ◽  
Fangjing Wu ◽  
Preetam Amancharla ◽  
Anupam Datta
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Gender Bias ◽  
Language Processing
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