Relevant bibliographies by topics / Race and gender bias

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Race and gender bias'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Race and gender bias"

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Garb, Howard N. "Race Bias, Social Class Bias, and Gender Bias in Clinical Judgment." Clinical Psychology: Science and Practice 4, no. 2 (1997): 99–120. http://dx.doi.org/10.1111/j.1468-2850.1997.tb00104.x.

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Garb, Howard N. "Race bias and gender bias in the diagnosis of psychological disorders." Clinical Psychology Review 90 (December 2021): 102087. http://dx.doi.org/10.1016/j.cpr.2021.102087.

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Koushik, Prasad. "Unveiling Bias: Implicit Bias in Hiring and Promotion Process." Journal of Research and Review in Human Resource Management & Labour Studies 1, no. 2 (2024): 39–49. https://doi.org/10.5281/zenodo.14033498.

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<em>This has a look at investigates the effect of implicit bias in hiring and advertising methods, focusing on race and ethnicity bias, capacity bias, and gender bias as unbiased variables influencing hiring decisions, the established variable. Implicit biases operate unconsciously and may considerably have an effect on recruitment and development opportunities within groups. This research explores how these biases manifest in diverse levels of the hiring and advertising process, together with activity postings, resume evaluations, interviews, and performance exams. The have a look at also ana
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Harris, Allison P., and Maya Sen. "Bias and Judging." Annual Review of Political Science 22, no. 1 (2019): 241–59. http://dx.doi.org/10.1146/annurev-polisci-051617-090650.

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How do we know whether judges of different backgrounds are biased? We review the substantial political science literature on judicial decision making, paying close attention to how judges' demographics and ideology can influence or structure their decision making. As the research demonstrates, characteristics such as race, ethnicity, and gender can sometimes predict judicial decision making in limited kinds of cases; however, the literature also suggests that these characteristics are far less important in shaping or predicting outcomes than is ideology (or partisanship), which in turn correla
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Thomas, Jonathan Norris, David Brown, Kristin Reeves, Molly H. Fisher, Cindy Jong, and Edna O. Schack. "Connections Between Pre-service Teachers’ Professional Noticing and Perceptions of Race and/or Gender." Education and Society 41, no. 2 (2023): 49–67. http://dx.doi.org/10.7459/es/41.2.04.

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This study examined potential bias with respect to perceived gender and race in pre-service teachers’ professional noticing of children’s mathematical thinking. The goal of the study was to explore emerging connections between professional noticing and equity concerns in mathematics education and discover the extent to which such noticing may be influenced by a student’s race and gender. A sample of 151 preservice teachers participated, and our findings suggest that bias tends to emerge in the interpreting phase of professional noticing; however, such emergence was not statistically significan
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Hoff, Lee Ann. "Comments on Race, Gender, and Class Bias in Nursing." Medical Anthropology Quarterly 8, no. 1 (1994): 96–99. http://dx.doi.org/10.1525/maq.1994.8.1.02a00080.

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Chávez, Kerry, and Kristina M. W. Mitchell. "Exploring Bias in Student Evaluations: Gender, Race, and Ethnicity." PS: Political Science & Politics 53, no. 2 (2019): 270–74. http://dx.doi.org/10.1017/s1049096519001744.

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ABSTRACTResearch continues to accumulate showing that in instructor evaluations students are biased against women. This article extends these analyses by examining the dynamics between evaluations and gender and race/ethnicity. In a quasi-experimental design, faculty members teaching identical online courses recorded welcome videos that were presented to students at the course onset, constituting the sole exposure to perceived gender and race/ethnicity. This enables exploration of whether and to what degree the instructors’ characteristics influenced student evaluations, even after holding all
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Berger, Mark C., and Darrell E. Glenn. "Selectivity bias and earnings differences by gender and race." Economics Letters 21, no. 3 (1986): 291–96. http://dx.doi.org/10.1016/0165-1765(86)90190-4.

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Liu, Jieli, and Haining Wang. "Assessing Gender and Racial Bias in Large Language Model‐Powered Virtual Reference." Proceedings of the Association for Information Science and Technology 61, no. 1 (2024): 576–80. http://dx.doi.org/10.1002/pra2.1061.

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ABSTRACTTo examine whether integrating large language models (LLMs) into library reference services can provide equitable services to users regardless of gender and race, we simulated interactions using names indicative of gender and race to evaluate biases across three different sizes of the Llama 2 model. Tentative results indicated that gender test accuracy (54.9%) and racial bias test accuracy (28.5%) are approximately at chance level, suggesting LLM‐powered reference services can provide equitable services. However, word frequency analysis showed some slight differences in language use ac
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Begic, Dinka, Clemens Janda-Martinac, Marija Vrdoljak, and Livia Puljak. "Reporting and analyses of sex/gender and race/ethnicity in randomized controlled trials of interventions published in the highest-ranking anesthesiology journals." Journal of Comparative Effectiveness Research 8, no. 16 (2019): 1417–23. http://dx.doi.org/10.2217/cer-2019-0071.

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Aim: We assessed reporting of data on sex/gender and race/ethnicity in randomized controlled trials of interventions published in the highest-ranking anesthesiology journals from 2014 to 2017. Methods: We extracted data regarding terminology for sex/gender, proportion of participants according to the race/gender and race/ethnicity, and results shown for the race/gender and race/ethnicity. Results: Among the analyzed 732 trials, few stratified allocation of participants on the basis of sex/gender and race/ethnicity, few reported results for sex/gender or race/ethnicity and the outcomes reported
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Dissertations / Theses on the topic "Race and gender bias"

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Clauhs, Matthew Scott. "The Effects of Race and Gender Bias on Style Identification and Music Evaluation." Diss., Temple University Libraries, 2013. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/236600.

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Music Education<br>Ph.D.<br>The purpose of this study was to examine how race and gender bias influence music educators' perceptions of musical style and evaluations of brief jazz and classical piano performances. Previous research has shown that race and gender bias and stereotype activation influence our judgment of others. These factors could result in biased evaluations of musical performances, including ensemble auditions and college level juries. I constructed an instrument designed to test these biases by experimentally manipulating race and gender variables of jazz and classical perfor
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Crowell, Robin April. "Gender Bias and the Evaluation of Players: Voice and Gender in Narrated Gameplay Videos." PDXScholar, 2016. http://pdxscholar.library.pdx.edu/open_access_etds/3156.

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This study evaluates perception differences of male and female narrators in video game tutorials. Video games have long been considered a masculine pursuit, and because of this, women have endured unpleasant surroundings and interactions in gaming and related communities. With the proliferation of technologies like Twitch and YouTube gaming, gaming is more communicative than ever, increasing potential for problematic interactions. Recent booms in these technologies emphasize the importance of understanding how varying demographics are perceived, as these perceptions influence interactions, pot
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Bolden, Adonis J. "An Examination of Teacher Bias in Special Education Referrals Based Upon Student Race and Gender." Ohio University / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1242323105.

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Haigh, Charles Frederick. "Gender differences in SAT scores : analysis by race and socioeconomic level." Virtual Press, 1995. http://liblink.bsu.edu/uhtbin/catkey/941574.

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Gender differences on Scholastic Aptitude Test (SAT) scores were analyzed by racial and socioeconomic groupings. Differences in SAT-Math scores, in SAT-Verbal scores, and in the difference between SAT-Math and SAT-Verbal scores were studied using four racial groupings (African American, Asian American, Caucasian American, and Hispanic American) and two socioeconomic groupings (average-to-high income and average-low income) of students. All differences were tested at the .05 level. Socioeconomic status was determined by using federal guidelines for free and reduced school lunches.The population
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Fisher, Amy E. "STUDENTS IDENTITIES AND TEACHER EXPECTATIONS: A FACTORIAL EXPERIMENT AT THE INTERSECTION OF RACE, GENDER, AND ABILITY." UKnowledge, 2019. https://uknowledge.uky.edu/edp_etds/85.

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Behavioral and academic outcomes differ for students by race, ability, and gender within the K-12 public education system. Moreover, striking gaps exist at the intersection of race, ability, and gender, despite the similarity in severity and frequency of behavior between groups. Few studies, however, have examined the educational mechanisms that contribute to these gaps. Despite this, the scientific literature? shows that when educators have high expectations, students are more likely to be successful academically and behaviorally. Therefore, this study examines the inverse of this relationshi
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Kline, Erika Danielle. "Managing Negative Behavior in a Diverse Workplace." OpenSIUC, 2021. https://opensiuc.lib.siu.edu/dissertations/1960.

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Managing diversity in the workplace is a challenging task for supervisors. Supervisors must punish negative behavior consistently, regardless their employees’ demographic characteristics. Some research suggests that negative workplace behaviors committed by lower status group members (e.g., Black people or women) are attributed to more internal factors and penalized more severely compared to higher status group members (e.g., White people or men; Duncan, 1979; Bowles & Gelfand, 2009; Luksyte, et al., 2013). However, recent evidence of pro-Black biases in judgments (Mendes & Koslov, 2013; Ziger
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Furrow, Ashley D. "Race and Gender Bias in Editorial and Advertising Photographs and in Sources in Sports Illustrated Kids, 2000-2009." Ohio University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1288385066.

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Hartman, Jennifer S. "ARE CLINICIANS BIASED? THE ROLE OF CLIENT VARIABLES IN CLINICIAN ASSESSMENT AND DIAGNOSIS OF DEPRESSIVE DISORDERS." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin998321388.

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Tarjimanyan, Arman. "Media Bias in Portrayal of Hillary Clinton and Barack Obama on Leading Television Networks During 2008 Democratic Nomination Race." Ohio University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1398268189.

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Hernandez, William. "Minority Bias in Supervisor Ratings: Comparing Subjective Ratings and Objective Measures of Job Performance." Wright State University / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=wright1357927094.

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Books on the topic "Race and gender bias"

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United, States Court of Appeals (District of Columbia Circuit) Task Force of the District of Columbia Circuit on Gender Race and Ethnic Bias. The Gender, Race, and Ethnic Bias Task Force project in the D.C. Circuit. The Task Force, 1995.

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1911-, Mensh Harry, ed. The IQ mythology: Class, race, gender, and inequality. Southern Illinois University Press, 1991.

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Roades, Laurie Ann. Gender and race bias in the diagnosis of major depressive episode and alcohol dependence. UMI, 1994.

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District of Columbia. Task Force on Racial and Ethnic Bias. Final report of the Task Force on Racial and Ethnic Bias and Task Force on Gender Bias in the Courts. District of Columbia Courts, 1992.

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United States. Court of Appeals (District of Columbia Circuit). Task Force of the D.C. Circuit on Gender, Race, and Ethnic Bias., ed. Preliminary report to the Task Force of the D.C. Circuit on Gender, Race, and Ethnic Bias. Special Committee on Gender, 1994.

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Charlotte, Johnson-Welch, and BASICS Project (Arlington, Va.), eds. Gender bias in health care among children 0-5 years: Opportunities for child survival programs. BASICS, 1997.

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Muthaliʼin, Achmad. Bias gender dalam pendidikan. Muhammadiyah University Press, 2001.

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Montgomery, Diane. Dyslexia and Gender Bias. Routledge, 2018. http://dx.doi.org/10.4324/9780429030130.

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Partini. Bias gender dalam birokrasi. Tiara Wacana, 2013.

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Mobilia, Boumil Marcia, ed. Law and gender bias. F.B. Rothman & Co., 1994.

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Book chapters on the topic "Race and gender bias"

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Austin, Michael L. "Is Siri a Little Bit Racist? Recognizing and Confronting Algorithmic Bias in Emerging Media." In Race/Gender/Class/Media. Routledge, 2019. http://dx.doi.org/10.4324/9781351630276-55.

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Ramachandran, Sreeraj, and Ajita Rattani. "Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups." In Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37731-0_40.

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Xu, Ke, Shera Potka, and Alex Thomo. "Gender and Race Bias in Consumer Product Recommendations by Large Language Models." In Lecture Notes on Data Engineering and Communications Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87766-7_22.

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Maldonado, Yvonne A., Magali Fassiotto, and Barbara Jérôme. "Implicit and Overt Race/Gender Bias: Diversity of Leadership in Healthcare and Health Sciences." In Leadership at the Intersection of Gender and Race in Healthcare and Science. Routledge, 2022. http://dx.doi.org/10.4324/9781003092551-10.

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Makhortykh, Mykola, Aleksandra Urman, and Roberto Ulloa. "Detecting Race and Gender Bias in Visual Representation of AI on Web Search Engines." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78818-6_5.

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Butts, Gary C., and Taylor Harrell. "Tool 1: Data to Drive Change and Address Implicit Bias at the Organizational Level." In Leadership at the Intersection of Gender and Race in Healthcare and Science. Routledge, 2022. http://dx.doi.org/10.4324/9781003092551-21.

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Kafkalias, Andreas, Stylianos Herodotou, Zenonas Theodosiou, and Andreas Lanitis. "Bias in Face Image Classification Machine Learning Models: The Impact of Annotator’s Gender and Race." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08337-2_8.

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Stahl, Bernd Carsten, Doris Schroeder, and Rowena Rodrigues. "Unfair and Illegal Discrimination." In Ethics of Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17040-9_2.

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AbstractThere is much debate about the ways in which artificial intelligence (AI) systems can include and perpetuate biases and lead to unfair and often illegal discrimination against individuals on the basis of protected characteristics, such as age, race, gender and disability. This chapter describes three cases of such discrimination. It starts with an account of the use of AI in hiring decisions that led to discrimination based on gender. The second case explores the way in which AI can lead to discrimination when applied in law enforcement. The final example looks at implications of bias
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Williams, Stacy A. S. "Bias, Race." In Encyclopedia of Child Behavior and Development. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-79061-9_329.

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Marwick, Thomas H., and Jonathan Chan. "Gender Bias." In Coronary Disease in Women. Humana Press, 2004. http://dx.doi.org/10.1007/978-1-59259-645-4_22.

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Conference papers on the topic "Race and gender bias"

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Iso, Hayate, Pouya Pezeshkpour, Nikita Bhutani, and Estevam Hruschka. "Evaluating Bias in LLMs for Job-Resume Matching: Gender, Race, and Education." In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.naacl-industry.55.

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You, Zhiwen, HaeJin Lee, Shubhanshu Mishra, et al. "Beyond Binary Gender Labels: Revealing Gender Bias in LLMs through Gender-Neutral Name Predictions." In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.gebnlp-1.16.

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Zhu, Shucheng, Bingjie Du, Jishun Zhao, Ying Liu, and Pengyuan Liu. "Do PLMs and Annotators Share the Same Gender Bias? Definition, Dataset, and Framework of Contextualized Gender Bias." In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.gebnlp-1.2.

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Dikshit, Malika, Houda Bouamor, and Nizar Habash. "Investigating Gender Bias in STEM Job Advertisements." In Proceedings of the 5th Workshop on Gender Bias in Natural Language Processing (GeBNLP). Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.gebnlp-1.11.

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Chen, Yuen, Vethavikashini Chithrra Raghuram, Justus Mattern, Rada Mihalcea, and Zhijing Jin. "Causally Testing Gender Bias in LLMs: A Case Study on Occupational Bias." In Findings of the Association for Computational Linguistics: NAACL 2025. Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.findings-naacl.281.

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Chen, Danqing, Adithi Satish, Rasul Khanbayov, Carolin Schuster, and Georg Groh. "Tuning Into Bias: A Computational Study of Gender Bias in Song Lyrics." In Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025). Association for Computational Linguistics, 2025. https://doi.org/10.18653/v1/2025.latechclfl-1.12.

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Kiritchenko, Svetlana, and Saif Mohammad. "Examining Gender and Race Bias in Two Hundred Sentiment Analysis Systems." In Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics. Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/s18-2005.

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Puc, Andraz, Vitomir Struc, and Klemen Grm. "Analysis of Race and Gender Bias in Deep Age Estimation Models." In 2020 28th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco47968.2020.9287219.

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Chauhan, Aadi, Taran Anand, Tanisha Jauhari, et al. "Identifying Race and Gender Bias in Stable Diffusion AI Image Generation." In 2024 IEEE 3rd International Conference on AI in Cybersecurity (ICAIC). IEEE, 2024. http://dx.doi.org/10.1109/icaic60265.2024.10433840.

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Buncher, John B., Jayson M. Nissen, Ben Van Dusen, Robert M. III Talbot, and Hannah Huvard. "Bias on the Force Concept Inventory across the intersection of gender and race." In 2021 Physics Education Research Conference. American Association of Physics Teachers, 2021. http://dx.doi.org/10.1119/perc.2021.pr.buncher.

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Reports on the topic "Race and gender bias"

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Blanchard, Alexander, and Laura Bruun. Bias in Military Artificial Intelligence. Stockholm International Peace Research Institute, 2024. https://doi.org/10.55163/cjft9557.

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To support states involved in the policy debate on military artificial intelligence (AI), this background paper provides a deeper examination of the issue of bias in military AI. Three insights arise. First, policymakers could usefully develop an account of bias in military AI that captures shared concern around unfairness. If so, ‘bias in military AI’ might be taken to refer to the systemically skewed performance of a military AI system that leads to unjustifiably different behaviours—which may perpetuate or exacerbate harmful or discriminatory outcomes—depending on such social characteristic
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Grown, Caren, and Giulia Mascagni. Towards Gender Equality in Tax and Fiscal Systems: Moving Beyond the Implicit-Explicit Bias Framework. Institute of Development Studies, 2024. http://dx.doi.org/10.19088/ictd.2024.015.

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The subject of gender and taxation has gained increasing traction in policy circles. Most existing evidence is based on the implicit and explicit bias framework developed in the mid-1990s. This framework has been useful in promoting research in this area, and tax reform to address gender biases. However, as explicit biases become increasingly rare, we argue that the framework is no longer fit for guiding policy towards improved tax equity and gender equality. Most importantly, the ‘tax-bias’ framing creates the impression that the solution to rectifying the underlying problem lies in reforming
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Muñoz, Juan Sebastián. Re-estimating the Gender Gap in Colombian Academic Performance. Inter-American Development Bank, 2014. http://dx.doi.org/10.18235/0011529.

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This paper presents evidence of the relationship between the disparity in the academic performance of boys and girls in Colombia and the country's excessively high school dropout rates. By using the OLS and trimming for bounds techniques, and based on data derived from the PISA 2009 database, the presented findings show that the vast majority of this gender-related performance gap is explained by selection problems in the group of low-skilled and poor male students. In particular, the high dropout rate overestimates male performance means, creating a selection bias in the regular OLS estimatio
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Blau, Francine, and Lawrence Kahn. Race and Gender Pay Differentials. National Bureau of Economic Research, 1992. http://dx.doi.org/10.3386/w4120.

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Hu, Luojia, and Christopher Taber. Layoffs, Lemons, Race, and Gender. National Bureau of Economic Research, 2005. http://dx.doi.org/10.3386/w11481.

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Taylor, Dorceta E. Race, class, gender, and American environmentalism. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2002. http://dx.doi.org/10.2737/pnw-gtr-534.

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Ewens, Michael. Race and Gender in Entrepreneurial Finance. National Bureau of Economic Research, 2022. http://dx.doi.org/10.3386/w30444.

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Matthew, Abigail, Amalia Miller, and Catherine Tucker. Algorithmic Bias and Historical Injustice: Race and Digital Profiling. National Bureau of Economic Research, 2024. http://dx.doi.org/10.3386/w32485.

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Hersh, Eitan, and Stephen Ansolabehere. Gender, Race, Age and Voting: A Research Note. Librello, 2013. http://dx.doi.org/10.12924/pag2013.01020132.

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Baccara, Mariagiovanna, Allan Collard-Wexler, Leonardo Felli, and Leeat Yariv. Child-Adoption Matching: Preferences for Gender and Race. National Bureau of Economic Research, 2010. http://dx.doi.org/10.3386/w16444.

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