Hiring Bias Research Articles

The presence of diversity initiatives leads to increased pro-White hiring decisions among conservatives.

Despite the push and pull between pro-diversity advocates and conservative resistance, most organizations have implemented diversity initiatives in an effort to promote equitable and fair organizational practices. Past work has shown that these diversity initiatives may not be as effective as expected and may instead result in unintended negative consequences for the very individuals they are meant to support. In three novel experiments (total N = 3,664), we investigated whether and when the presence of pro-diversity messages in organizational job recruitment materials might facilitate versus hinder the hiring of underrepresented racial minorities. Participant race and political ideology were also investigated as predictors of hiring recommendations. Findings indicate that pro-diversity messages facilitate politically motivated hiring bias. Specifically, in the presence of pro-diversity messages, White and some Black, Indigenous, and people of color (BIPOC) conservatives tend to display a pro-White shift in their hiring recommendations, whereas liberals tend to display a pro-minority shift. The present research underscores the importance of organizational awareness regarding the potential for hiring managers to react, whether consciously or subconsciously, against pro-diversity efforts because of political ideology. The present research also highlights the need for organizations to move beyond just espousing pro-diversity values and actually investigate the impact diversity initiatives have on hiring, retention, and promotion of diverse employees. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Monitoring and Home Bias in Global Hiring: Evidence from an Online Labor Platform

Online labor platforms increasingly use monitoring systems to manage remote workers. This study assesses whether and how these systems mitigate employer bias in hiring foreign versus domestic workers. Leveraging the exogenous introduction of a monitoring system for time-based projects on a leading online labor platform, we employ a difference-in-differences model to estimate the impact of monitoring systems on mitigating employers’ tendency to bias against hiring foreign workers (home bias). Results indicate a significant reduction in home bias, along with a 15% increase in the hiring of foreign workers following the introduction of the monitoring system. The mitigation effect is notably stronger in high-routine projects or when employers lack prior positive experiences with foreign workers, two scenarios characterized by low external uncertainty and high internal uncertainty, respectively. Moreover, employers no longer exhibit a stronger home bias in scenarios of lower moral hazard risk or coordination costs. These findings lend support to the effectiveness of monitoring systems in mitigating employers’ home bias through facilitating contractual control and coordination. Our study offers important implications for the design of online labor platforms and policymaking.

Can agentic messages help? Linguistic strategies to counteract voice-based sexual orientation discrimination.

Gay men who believe to sound 'gay' expect to be discriminated against because of their voices and gay-sounding men are discriminated against in the hiring process. We examined whether uttering an agency-based message decreased discrimination expectancy and enactment. In Study 1a (N = 256; gay and bisexual men) and Study 1b (N = 216; gay men), speakers uttered agentic (vs. neutral) messages. We assessed their self-perception as gay sounding, agency self-attribution and discrimination expectancy. Uttering agentic (vs. neutral) messages made the speakers self-perceive as more agentic and this decreased discrimination expectancy. Additionally, self-perception as gay sounding predicted discrimination expectancy. In Study 2 (N = 466), heterosexual participants listened to gay- and straight-sounding speakers uttering either neutral or agentic messages and rated them in terms of agency and employability. Gay-sounding speakers uttering agentic messages were less likely to be discriminated against than when uttering neutral messages. Results show the positive impact of linguistic strategies involving agentic messages to reduce discrimination expectancy and hiring biases.

A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring

The study comprehensively reviews artificial intelligence (AI) techniques for addressing algorithmic bias in job hiring. More businesses are using AI in curriculum vitae (CV) screening. While the move improves efficiency in the recruitment process, it is vulnerable to biases, which have adverse effects on organizations and the broader society. This research aims to analyze case studies on AI hiring to demonstrate both successful implementations and instances of bias. It also seeks to evaluate the impact of algorithmic bias and the strategies to mitigate it. The basic design of the study entails undertaking a systematic review of existing literature and research studies that focus on artificial intelligence techniques employed to mitigate bias in hiring. The results demonstrate that the correction of the vector space and data augmentation are effective natural language processing (NLP) and deep learning techniques for mitigating algorithmic bias in hiring. The findings underscore the potential of artificial intelligence techniques in promoting fairness and diversity in the hiring process with the application of artificial intelligence techniques. The study contributes to human resource practice by enhancing hiring algorithms’ fairness. It recommends the need for collaboration between machines and humans to enhance the fairness of the hiring process. The results can help AI developers make algorithmic changes needed to enhance fairness in AI-driven tools. This will enable the development of ethical hiring tools, contributing to fairness in society.

Nonverbal facial cues signaling sexually transmitted infections cause dehumanization and discrimination

Dehumanization often underlies the social ostracism, exclusion, and discrimination experienced by stigmatized group members. Given findings that people can detect sexually transmitted infection (STI) status from nonverbal facial cues, we tested whether people would dehumanize and discriminate against STI-positive individuals from detecting their stigmatized status. Specifically, we hypothesized that nonverbal stigma cues would stimulate dehumanizing reactions that lead to biases against hiring STI-positive individuals. Results showed that people dehumanize STI-positive individuals based on their nonverbal stigma cues (i.e., negative affect; Study 1), except when STI status is explicitly disclosed (Study 2), which leads to potential hiring biases (Study 3). Dehumanization and discrimination against STI-positive individuals may therefore depend on the stigma's legibility from nonverbal cues but may be tempered by explicit information about STI status.

Gender Bias in Hiring: An Analysis of the Impact of Amazon's Recruiting Algorithm

Algorithmic bias in artificial intelligence (AI) is a growing concern, especially in the employment sector, where it can have devastating effects on both individuals and society. Gender discrimination is one of the most prevalent forms of algorithmic bias seen in numerous industries, including technology. The underrepresentation of women in the field of information technology is a well-known issue, and several organizations have made tackling this issue a top priority. Amazon, one of the world's top technology businesses, has been at the forefront of initiatives to increase inclusiveness and diversity in the sector. Concerns exist, however, that algorithmic bias in their recruitment process may perpetuate discrimination based on gender. This study intends to investigate these issues by employing an interpretive epistemology and utilizing interviews and focus groups to acquire a more nuanced knowledge of the subject,with keyfactors contributing to algorithmic gender bias in Amazon's recruitmentprocess andrecommend strategies for improving women's employment in information technology.

Marijuana Use and Perceptions of Employment Suitability

Abstract: The present study extends research on marijuana social acceptance and employment suitability in the United States by examining how hiring managers view substance use-related content in social networking profiles. Specifically, this research focused on the weight hiring managers placed on social networking posts containing recreational marijuana content, medicinal marijuana content, and alcohol content when assessing potential candidates. With a sample of 405 hiring managers who evaluated experimentally manipulated social networking profiles, the results demonstrated a modest negative hiring bias against recreational marijuana content, with stronger negative effects for alcohol content. At the same time, social networking content related to medicinal marijuana content did not have a significant effect on perceptions of employment suitability. These findings highlight the nuanced nature of substance use stigma in today’s evolving society.

Biased Evaluative Descriptions

AbstractIn this essay I identify a type of linguistic phenomenon new to feminist philosophy of language: biased evaluative descriptions. Biased evaluative descriptions are descriptions whose well-intended positive surface meanings are inflected with implicitly biased content. Biased evaluative descriptions are characterized by three main features: (1) they have roots in implicit bias or benevolent sexism, (2) their application is counterfactually unstable across dominant and subordinate social groups, and (3) they encode stereotypes. After giving several different kinds of examples of biased evaluative descriptions, I distinguish them from similar linguistic concepts, including backhanded compliments, slurs, insults, epithets, pejoratives, and dog whistles. I suggest that the traditional framework of Gricean implicature cannot account for biased evaluative descriptions. I discuss some challenges to the distinctiveness and evaluability of biased evaluative descriptions, including intersectional social identities. I conclude by discussing their social significance and moral status. Identifying biased evaluative descriptions is important for a variety of social contexts, from the very general and broad (political speeches) to the very particular and small (bias in academic hiring).

Confidence, Connection & Collaboration: Creating a Scalable Bias Reduction Improvement Coaching Train-the-Trainer Program to Mitigate Implicit Bias across a Medical Center

Problem: Academic medical centers need to mitigate the negative effects of implicit bias with approaches that are empirically-based, scalable, sustainable, and specific to departmental needs. Guided by Kotter’s Model of Change to create and sustain cultural change, we developed the Bias Reduction Improvement Coaching Program (BRIC), a two-year, train-the-trainer implicit bias coaching program designed to meet the increasing demand for bias training across a university medical center. Intervention: BRIC trained a cohort of faculty and staff as coaches during four quarterly training sessions in Year 1 that covered 1) the science of bias, 2) bias in selection and hiring, 3) bias in mentoring, and 4) bias in promotion, retention, and workplace culture. In Year 2, coaches attended two booster sessions and delivered at least two presentations. BRIC raises awareness of bias mitigation strategies in a scalable way by uniquely building capacity through department-level champions, providing programming that addresses the ‘local context,’ and setting a foundation for sustained institutional change. Context: In a U.S. academic medical center, 27 faculty and staff from 24 departments were trained as inaugural BRIC coaches. We assessed outcomes at multiple levels: BRIC coach outcomes (feedback on the training sessions; coach knowledge, attitudes, and skills), departmental-level outcomes (program attendee feedback, knowledge, and intentions) and institutional outcomes (activities to sustain change). Impact: After Year 1, coaches reported high satisfaction with BRIC and a statistically significant increase in self-efficacy in their abilities to recognize, mitigate, and teach about implicit bias. In Year 2, attendees at BRIC coach presentations reported an increase in bias mitigation knowledge, and the majority committed to taking follow-up action (e.g., taking an Implicit Association Test). Coaches also launched activities for sustaining change at the broader university and beyond. Lessons Learned: The BRIC Program indicates a high level of interest in receiving bias mitigation training, both among individuals who applied to be BRIC coaches and among presentation attendees. BRIC’s initial success supports future expansion. The model appears scalable and sustainable; future efforts will formalize the emerging community of practice around bias mitigation and measure elements of on-going institutional culture change.

Unpacking (un)conscious social class bias in faculty hiring processes in Chile: PhD prestige granting university and network

AbstractSocial class of origin is apparently an imperceptible attribute among doctorate holders seeking academic jobs. Yet, recent studies in different countries reveal that social class of origin may still be influencing the chances of PhD holders from low social class being hired at prestigious universities. Drawing from the theory of social and cultural reproduction, normative ‘fair’ academic hirings frameworks, and qualitative evidence collected in Chile, this research identifies the mechanisms that trigger (un)conscious social class bias in the stages of recruitment and selection of candidates in seven academic departments in economics and industrial engineering (46 interviews). Findings did not prove explicit classism manifestations, but practices of inclusion/exclusion of candidates based on the prestige of PhD‐granting universities, and networks. These reproduce the relationship between social class and unequal chances of being connected to prestigious universities. Recommendations to address (un)conscious social class bias in academic hiring are discussed.

Encouraging self-blinding in hiring

One strategy for minimizing bias in hiring is blinding—purposefully limiting the information used when screening applicants to that which is directly relevant to the job and does not elicit bias based on race, gender, age, or other irrelevant characteristics. Blinding policies remain rare, however. An alternative to blinding policies is self-blinding, in which people performing hiring-related evaluations blind themselves to biasing information about applicants. Using a mock-hiring task, we tested ways to encourage self-blinding that take into consideration three variables likely to affect whether people self-blind: default effects on choices, people’s inability to assess their susceptibility to bias, and people’s tendency not to recognize the full range of information that can elicit that bias. Participants with hiring experience chose to receive or be blind to various pieces of information about applicants, some of which were potentially biasing. They selected potentially biasing information less often when asked to specify the applicant information they wanted to receive than when asked to specify the information they did not want to receive, when prescribing selections for other people than when making the selections for themselves, and when the information was obviously biasing than when it was less obviously so. On the basis of these findings, we propose a multipronged strategy that human resources leaders could use to enable and encourage hiring managers to self-blind when screening job applicants.

Impact of Gender-Inclusive Labor Policies on Women’s Employment in Isiolo County, Kenya

The gender gap in Kenya’s labour market has traditionally been in favour of men, with women being underrepresented in the formal segment of the market. Kenya has gradually come up with employment policies to rectify this problem and achieve gender equality in the market. While these policies are formulated at national level, part of the implementation is entrusted to county governments. However, the impact or effectiveness of these policies to achieve intended goal has hardly been interrogated mainly the context of county governments in Kenya. The study investigated the influence of gender inclusive labour policies on women employment, with a precise case study of Isiolo County. This study was be anchored on the Empowerment Theory which argues that actions of empowerment ought to yield outcomes. The study adopted a descriptive survey method, targeting women working with the county government of Isiolo within its three Sub Counties namely, Isiolo, Merti, and Garbatulla. The study found that gender- inclusive labour policies in Isiolo County have had mixed success. While most respondents believed that implementing these policies has increased the number of women in the workplace or leadership positions, there still needs to be more awareness. Most respondents reported experiencing discrimination, including hiring bias, promotion denial, and unequal pay. The study also revealed barriers to women’s career progression in the public sector, such as gender bias, lack of access to training, and work-life balance challenges. Moreover, respondents perceived the effectiveness of gender-inclusive labour policies as ineffective. A lack of awareness, insufficient training, and resistance to change are the main factors hindering their implementation. Further research and targeted interventions are recommended to address these issues, including promoting intersectionality, addressing unconscious bias, engaging male allies, and examining the effects of remote and flexible work arrangements.

MAKING THE CASE FOR AGE INCLUSIVITY IN HIGHER EDUCATION

Abstract Often viewed as institutions primarily servings 18-to-24-year-old student populations, U.S. colleges and universities are age-diverse. In our recent national study of AFU institutions, 21 campuses maintaining age data reported that 12,718 faculty (39.24% of faculty) and 20,361 staff (42.31% of staff) were ages 50+. Additionally, 22 campuses reported 7,080 students (1.58%) ages 50+. Despite higher education’s attention to diversity, equity, and inclusion (DEI), age is often overlooked; therefore, universities may need strategies for improving age inclusivity. Across 2,447 open-ended survey responses from our study, faculty, staff, and students describe experiences with age inclusivity (e.g., feeling valued) and exclusion (e.g., feeling unwelcome), and many call for greater sensitivity to aging in existing DEI efforts, such as more age-inclusive language in classrooms and attention to age bias in hiring and promotions. In addition, this presentation will examine responses that raise important considerations for integrating age inclusivity with other higher education DEI efforts.

Gender Bias in Stem Hiring: Implicit In-Group Gender Favoritism Among Men Managers

Women’s underrepresentation in science, technology, engineering, and mathematics (STEM) is related to the hierarchical social structure of gender relations in these fields. However, interventions to increase women’s participation have focused primarily on women’s interests rather than on STEM managers’ hiring practices. In this research, we examine STEM hiring practices, explore the implicit bias in criteria used by STEM managers, and suggest possible corrective solutions. Using an experimental design with 213 men and women STEM managers, we show that when evaluating a female candidate, women and men STEM managers apply differential selection criteria, with men demonstrating implicit in-group gender favoritism in their hiring decisions. Specifically, the ability to work long hours was a more important criterion for male managers when evaluating a female candidate, forming an implicit gender bias, whereas female managers gave greater importance to problem-solving ability, a more gender-equal criterion. Adding a personal note to the curriculum vitae stating that the candidate had hired a full-time nanny was useful in decreasing the importance of the ability to work long hours criterion for men managers. We suggest individual and institutional interventions to reduce this bias, as a path to increasing women’s participation in STEM.

Ungendered writing: Writing styles are unlikely to account for gender differences in funding rates in the natural and technical sciences

• Structural differences in funding rates between male and female scientists have been reported. • We study the potential influence of gendered differences in writing style on funding decisions • Unlike previous studies, we only find minimal differences in writing style between the sexes, using full-text applications. • We conclude that writing styles are unlikely to account for skewed funding patterns in the natural and technical sciences Academia has traditionally faced a substantial gender gap in staff positions and career path progression. Women do not advance up the academic career ladder in the same rate as men, with evidence of gender bias in hiring, earnings, funding, and recognition by means of prestigious awards. In this study we focus on gender differences in funding applications. Multiple factors have been proposed as potentially underlying mechanisms creating differences in funding rates between men and women, including bias in peer review processes and differences in language use. In this study we use a set of 1560 full-text applications in the natural and technical sciences that were subjected to a double-blind review process at a Danish private funder to analyse gendered writing as a potential factor causing differences in funding rates. Reproducing analyses from previous studies that found significant differences in writing styles, we analyse patterns in the use of positive words, levels of readability, concreteness and sentiment. Unlike previous studies, we only find minimal differences in writing style between the sexes. We conclude that writing styles are unlikely to account for skewed funding patterns and suggest ways in which funding programmes can be designed to provide fair opportunities to all applicants.

Résumé screening heuristic outcomes: an examination of hiring manager evaluation bias

PurposeHiring managers commonly rely on system-justifying motives and attitudes during résumé screening. Given the prevalent use of modern résumé formats (e.g. LinkedIn) that include not only an applicant's credentials but also headshot photographs, visible sources of information such as an applicant's race are also revealed while a hiring manager simultaneously evaluates a candidate's suitability. As a result, such screening is likely to activate evaluation bias. The purpose of this paper is to examine the role of a hiring manager's perceptions of race-system justification, that is, support for the status quo in relations between Black and White job candidates in reinforcing or mitigating hiring bias related to in-group and out-group membership during résumé screening.Design/methodology/approachDrawing from system justification theory (SJT) in a pre-selection context, in an experimental study involving 174 human resource managers, the authors tested two boundary conditions of the expected relationship between hiring manager and job candidate race on candidate ratings: (1) a hiring manager's affirmative action (AA) attitudes and system-justifying attitudes and (2) a job candidate's manipulated suitability for a position. This approach enabled us to juxtapose the racial composition of hiring manager–job candidate dyads under conditions in which the job candidate's race and competency for a posted position were manipulated to examine the conditions under which White and Black hiring managers are likely to make biased evaluations. The authors largely replicated these findings in two follow-up studies with 261 students and 361 online raters.FindingsThe authors found that information on a candidate's objective suitability for a job resulted in opposite-race positive bias among Black evaluators and same-race positive bias among White evaluators in study 1 alone. Conversely, positive attitudes toward AA policies resulted in in-group favoritism and strengthened a positive same-race bias for Black evaluators (study 1 and 2). We replicated this finding with a third sample to directly test system-justifying attitudes (study 3). The way in which White raters rated White candidates reflected the same attitudes against systems (AA attitudes) that Black raters rating Black candidates exhibited in the authors’ first two studies. Positive system-justifying attitudes or positive attitudes toward AA did not, however, translate into the elevation of same-race candidate ratings of suitability above those of opposite-race candidates.Research limitations/implicationsAlthough the size of the sample is on par with the percentage of Blacks nationwide in private-sector managerial-level positions ideally, the authors would have preferred to oversample Black HR managers. Given the scarcity of focus on Black HR managers, future researchers, using diverse samples of evaluators should also consider not only managers' and candidates' race but also their social dominance orientation. Moreover, it is important that future researchers use more racially diverse samples from other industries to more fully identify the ways in which the dynamics of system-justifying processes can emerge to influence evaluation bias during résumé screening.Practical implicationsAdvances in technology pose new challenges to HR hiring practices. This study attempts to fill a void regarding the unintended effects of bias during digital résumé screening. These trends have important HR implications. Initial screening of a job applicant's credentials while concurrently viewing the individual's photograph is likely to activate subconscious evaluation bias, produces inaccurate applicant ratings. This study's findings should caution hiring managers about the potential for bias to arise when viewing job candidates' digital résumés and encourage them to carefully examine various boundary conditions on racial similarity bias effects on applicant pre-screening and subsequent hiring decisions.Social implicationsThe study’s results suggest that bias might be attenuated as organizational leaders engage in efforts to understand their system-justifying motives and examine perceptions of the workplace social hierarchy (i.e. responses to status hierarchies) linked to perceptions of the status quo. For example, understanding how system justifying motives influence evaluation bias will inform how best to design training and other interventions that link discussions of workforce diversity to the relationships among groups within the organization's social hierarchy. This line of research should be further explored to better understand the complex forces at work when hiring managers adopt system-justifying motives during hiring evaluations.Originality/valueThe authors address the limitations of prior research by examining interactions between boundary conditions in a real-world context using real human resources hiring managers and more contemporary personnel-screening practices to test changes in the direction and strength of the relationship between hiring manager–job candidate race and hiring manager evaluations. Thus, the authors’ findings have implications for hiring bias and understanding of system-justification processes, particularly regarding how, when and why hiring managers support the status quo (i.e. perpetuate inequity) even if they are disadvantaged as a result.

BENJAMIN SACHS INTERVIEWED BY MARTHA MINOW

BENJAMIN SACHS INTERVIEWED BY MARTHA MINOW

Using Search Advocates to Mitigate Bias in Hiring: An Interview with Anne Gillies

A commitment to Diversity, Equity and Inclusion requires eliminating bias in hiring. Eliminating bias requires training, tools and cultural change. This interview with Anne Gillies of Oregon State University’s Search Advocates provides a nuanced understanding of how search practices reinforce bias and how she has operationalized a program that has the capacity to create deep cultural change. Interviewed by Emily Weak of Hiring Librarians, she outlines her program’s philosophy, as well as its origins and the challenges in measuring outcomes. Her program has found footing in academic libraries, but managers in public and other library types will benefit from understanding her methods.

Gendered Information in Resumes and its Role in Algorithmic and Human Hiring Bias

We study the extent and nature of gendered information in resumes, and its role in hiring bias using a predictive modeling approach. We first train a series of machine learning (ML) models to inductively learn gendered information in resumes using a matched sample set of resumes from technology firms (348k resumes). We then use these models to quantify the extent and nature of gendered information in resumes and develop a measure of gender incongruence -- a predictive measure of how much the self-presented gender characteristics in the resume deviate from the gender of the candidate. Using this measure of gender incongruence along with historical hiring data from technology firms, we test whether applicants whose resume gender characteristics deviate from their actual gender (i.e. male resumes with feminine characteristics, female resumes with masculine characteristics) are less likely to receive a callback. Our results show that (1) there is a significant amount of gendered information in resumes -- even among applicants that apply to the same job opening with similar job-relevant characteristics, ML models can learn to differentiate between genders with a high degree of accuracy (AUROC=0.81). (2) This gendered information has an impact on hiring outcomes -- women whose resume characteristics deviate from the norms of their own gender are less likely to receive a callback after controlling for job-relevant characteristics. We discuss these findings in light of algorithmic and human bias in hiring.

Can rubrics combat gender bias in faculty hiring?

Some bias persiste d, but rubric use should be encouraged.