Gendered STEM Workforce in the United Kingdom:The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Gendered STEM Workforce in the United Kingdom: The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress towards gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Gender, International Training and Ethnic Visibility: An Intersectional Approach to Studying Engineers in Canada.

Engineering in Canada has a large proportion of internationally-trained professionals. This reflects the rapid globalization of many professional fields in nations that have migration policies designed to attract the "best and brightest". Engineering is also a highly male-dominated field where women are tokens (below 15%) who may face a "chilly climate" as a result of their numeric underrepresentation and perceived occupational "inappropriateness". Empirical research that examines the transferability of immigrants' skills often highlights the risk of occupational mismatch or underemployment. Research on immigrant engineers' careers is usually restricted to studying men, and the career prospects of immigrant women engineers are understudied. This dissertation aims to address this gap by using intersectionality as a framework to examine immigrant women's combined vulnerabilities as internationally-trained professionals re-establishing their careers in a new country and as female tokens in a male-dominated field. Drawing on the nationally-representative 2006 Canadian census data, a series of multinomial logistic regressions are carried out to predict the likelihood of individuals with engineering training being successful in gaining entrance to: (1) the Canadian labour market; (2) the field of engineering; and (3) advanced positions within the engineering field. The intersection of gender, origin of training and ethnic visibility is examined by modeling the combined interacting effects of these three status variables. The results demonstrate that gender and, immigration and visible minority statuses work as independent and intersecting forces. Women, immigrants and visible minorities, each, are at a disadvantage in obtaining these different career outcomes. The intersections between the statuses create complex and diverse trajectories of disadvantage showing that the experiences of immigrant women engineers cannot be understood by studying immigrant men or women as a homogeneous group. Specifically, immigrant women are at a cumulative disadvantage in their chances of obtaining any employment, employment in engineering, and securing advanced positions within the field. Moreover, the analysis suggests that the cumulative disadvantage of immigrant and visible minority female engineers is produced by different forces. The results of this study highlight the relevance of the intersectionality framework in studying immigrant women professionals and offers important methodological considerations in studying occupational match versus mismatch

BIAS Word inventory for work and employment diversity, (in)equality and inclusivity (Version 1.0)

he language used in job advertisements contains explicit and implicit cues, which signal employers’ preferences for candidates of certain ascribed characteristics, such as gender and ethnicity/race. To capture such biases in language use, existing word inventories have focused predominantly on gender and are based on general perceptions of the ‘masculine’ or ‘feminine’ orientations of specific words and socio-psychological understandings of ‘agentic’ and ‘communal’ traits. Nevertheless, these approaches are limited to gender and they do not consider the specific contexts in which the language is used. To address these limitations, we have developed the first comprehensive word inventory for work and employment diversity, (in)equality, and inclusivity that builds on a number of conceptual and methodological innovations. The BIAS Word Inventory was developed as part of our work in an international, interdisciplinary project – BIAS: Responsible AI for Labour Market Equality – in Canada and the United Kingdom (UK). Conceptually, we rely on a sociological approach that is attuned to various documented causes and correlates of inequalities related to gender, sexuality, ethnicity/race, immigration and family statuses in the labour market context. Methodologically, we rely on ‘expert’ coding of actual job advertisements in Canada and the UK, as well as iterative cycles of inter-rater verification. Our inventory is particularly suited for studying labour market inequalities, as it reflects the language used to describe job postings, and the inventory takes account of cues at various dimensions, including explicit and implicit cues associated with gender, ethnicity, citizenship and immigration statuses, role specifications, equality, equity and inclusivity policies and pledges, work-family policies, and workplace context

BIAS Word inventory for work and employment diversity, (in)equality and inclusivity (Version 1.0)

he language used in job advertisements contains explicit and implicit cues, which signal employers’ preferences for candidates of certain ascribed characteristics, such as gender and ethnicity/race. To capture such biases in language use, existing word inventories have focused predominantly on gender and are based on general perceptions of the ‘masculine’ or ‘feminine’ orientations of specific words and socio-psychological understandings of ‘agentic’ and ‘communal’ traits. Nevertheless, these approaches are limited to gender and they do not consider the specific contexts in which the language is used. To address these limitations, we have developed the first comprehensive word inventory for work and employment diversity, (in)equality, and inclusivity that builds on a number of conceptual and methodological innovations. The BIAS Word Inventory was developed as part of our work in an international, interdisciplinary project – BIAS: Responsible AI for Labour Market Equality – in Canada and the United Kingdom (UK). Conceptually, we rely on a sociological approach that is attuned to various documented causes and correlates of inequalities related to gender, sexuality, ethnicity/race, immigration and family statuses in the labour market context. Methodologically, we rely on ‘expert’ coding of actual job advertisements in Canada and the UK, as well as iterative cycles of inter-rater verification. Our inventory is particularly suited for studying labour market inequalities, as it reflects the language used to describe job postings, and the inventory takes account of cues at various dimensions, including explicit and implicit cues associated with gender, ethnicity, citizenship and immigration statuses, role specifications, equality, equity and inclusivity policies and pledges, work-family policies, and workplace context