國立虎尾科技大學 |

Measuring Perceptions and Mitigating Bias in Text and Voice.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Measuring Perceptions and Mitigating Bias in Text and Voice./
作者:	Cryan, Jennifer Rose.
面頁冊數:	1 online resource (216 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
Contained By:	Dissertations Abstracts International85-02B.
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9798380140775

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

Measuring Perceptions and Mitigating Bias in Text and Voice. - 1 online resource (216 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380140775Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Human-computer interactionIndex Terms--Genre/Form:

554714
Electronic books.

Measuring Perceptions and Mitigating Bias in Text and Voice.
LDR:03380ntm a22003977 4500 001 1142079
005 20240414211939.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798380140775
035 $a (MiAaPQ)AAI30574922
035 $a AAI30574922
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Cryan, Jennifer Rose. $3 1466247
245 1 0 $a Measuring Perceptions and Mitigating Bias in Text and Voice.
264 0 $c 2023
300 $a 1 online resource (216 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-02, Section: B.
500 $a Advisor: Zhao, Ben;Zheng, Heather.
502 $a Thesis (Ph.D.)--The University of Chicago, 2023.
504 $a Includes bibliographical references
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.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Information technology. $3 559429
653 $a Human-computer interaction
653 $a Measuring perceptions
653 $a Gender stereotypes
653 $a Unbiased communication
653 $a Crowd-sourced method
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0800
690 $a 0489
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Chicago. $b Computer Science. $3 845504
773 0 $t Dissertations Abstracts International $g 85-02B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30574922 $z click for full text (PQDT)