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Measuring Perceptions and Mitigating Bias in Text and Voice.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Measuring Perceptions and Mitigating Bias in Text and Voice.
Author:	Cryan, Jennifer Rose.
Published:	Ann Arbor : ProQuest Dissertations & Theses, 2023
Description:	216 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Notes:	Advisor: Zhao, Ben;Zheng, Heather.
Contained By:	Dissertations Abstracts International85-02B.
Subject:	Computer science.
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30574922
ISBN:	9798380140775

Measuring Perceptions and Mitigating Bias in Text and Voice.
Cryan, Jennifer Rose.

Measuring Perceptions and Mitigating Bias in Text and Voice. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 216 p.

Source: Dissertations Abstracts International, Volume: 85-02, Section: B.

Thesis (Ph.D.)--The University of Chicago, 2023.

This item must not be sold to any third party vendors.

Our ability as humans to effectively communicate depends heavily on the language we use and the way we speak to one another. The values of our society are both reflected in and reinforced by our use of language. Detecting how language could reflect biases needs to remain effective as these values evolve over time. This dissertation evaluates methods to measure how people perceive written text and spoken voice, how these perceptions may perpetuate societal stereotypes, and how to prevent biased perceptions.Specifically, gendered language in text often affirms gender stereotypes and perpetuates bias and discrimination. As readers absorb written content, gendered language used settings such as biographies, recommendation letters, and job advertisements can negatively impact the subjects. Gender stereotypes have been studied extensively, however, the current methods used today still rely on word banks from nearly 50 years ago. Since then, societal views have continued to evolve and it's important to be able to reflect these changes. Additionally, significant advances have been made in developing new methods for analyzing how words are used in larger bodies of text. To address this, I first examine how descriptive language reflects societal perceptions of gender roles. Then, I demonstrate a crowd-sourced method for updating gender lexicons to reflect modern language and train deep learning models to detect gendered language more efficiently.In addition to written text, efficient and unbiased communication depends upon not only the content, but the manner in which it is presented. The tone of voice of a speaker can heavily influence how they are perceived (e.g., perceived trustworthiness, competence). Further, changes in emotion tone of voice can reduce biases and more effective communication. This work explores ways to improve methods for measuring perceptions of gendered language in text and emotion tone in voice, and ways to mitigate resulting biases.

ISBN: 9798380140775Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Human-computer interaction

Measuring Perceptions and Mitigating Bias in Text and Voice.
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502 $a Thesis (Ph.D.)--The University of Chicago, 2023.
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520 $a Our ability as humans to effectively communicate depends heavily on the language we use and the way we speak to one another. The values of our society are both reflected in and reinforced by our use of language. Detecting how language could reflect biases needs to remain effective as these values evolve over time. This dissertation evaluates methods to measure how people perceive written text and spoken voice, how these perceptions may perpetuate societal stereotypes, and how to prevent biased perceptions.Specifically, gendered language in text often affirms gender stereotypes and perpetuates bias and discrimination. As readers absorb written content, gendered language used settings such as biographies, recommendation letters, and job advertisements can negatively impact the subjects. Gender stereotypes have been studied extensively, however, the current methods used today still rely on word banks from nearly 50 years ago. Since then, societal views have continued to evolve and it's important to be able to reflect these changes. Additionally, significant advances have been made in developing new methods for analyzing how words are used in larger bodies of text. To address this, I first examine how descriptive language reflects societal perceptions of gender roles. Then, I demonstrate a crowd-sourced method for updating gender lexicons to reflect modern language and train deep learning models to detect gendered language more efficiently.In addition to written text, efficient and unbiased communication depends upon not only the content, but the manner in which it is presented. The tone of voice of a speaker can heavily influence how they are perceived (e.g., perceived trustworthiness, competence). Further, changes in emotion tone of voice can reduce biases and more effective communication. This work explores ways to improve methods for measuring perceptions of gendered language in text and emotion tone in voice, and ways to mitigate resulting biases.
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