Mind the gender gap: Inequalities in the emergent professions of artificial intelligence (AI) and data science

• Published date: 01 November 2023
• Author: Erin Young,Judy Wajcman,Laila Sprejer
• Date: 01 November 2023
• DOI: http://doi.org/10.1111/ntwe.12278

Received: 1 December 2022

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Accepted: 3 August 2023

DOI: 10.1111/ntwe.12278

RESEARCH ARTICLE

Mind the gender gap: Inequalities in the

emergent professions of artificial

intelligence (AI) and data science

Erin Young|Judy Wajcman|Laila Sprejer

Public Policy Programme, The Alan

Turing Institute, London, UK

Correspondence

Judy Wajcman

Email: j.wajcman@lse.ac.uk

Abstract

The emergence of new prestigious professions in data

science and artificial intelligence (AI) provide a rare

opportunity to explore the gendered dynamics of

technical careers as they are being formed. In this

paper, we contribute to the literature on gender

inequality in digital work by curating and analysing a

unique cross‐country data set. We use innovative data

science methodology to investigate the nature of work

and skills in these under‐researched fields. Our

research finds persistent disparities in jobs, qualifica-

tions, seniority, industry, attrition and even self‐

confidence in these fields. We identify structural

inequality in data and AI, with career trajectories of

professionals differentiated by gender, reflecting the

broader history of computing. Our work is original in

illuminating gendering processes within elite high‐tech

jobs as they are being configured. Paying attention to

these nascent fields is crucial if we are to ensure that

women take their rightful place at forefront of

technological innovation.

KEYWORDS

artificial intelligence, careers, data science, gender, inequalities,

professionalisation

New Technol Work Employ. 2023;38:391–414. wileyonlinelibrary.com/journal/ntwe

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and

reproduction in any medium, provided the original work is properly cited.

© 2023 The Authors. New Technology, Work and Employment published by Brian Towers (BRITOW) and John Wiley & Sons Ltd.

INTRODUCTION

The question of how automation will affect the nature of jobs, skills, occupations, professions

and labour markets is once again in the spotlight. The rise of generative artificial intelligence

(AI)—widely associated with ChatGPT—is the new focal point of excitement and anxiety.

1

The

novelty follows a familiar pattern: we are at an inflection point in relation to both job loss and

job creation, as we head towards an unrecognisable world of work. While the extent of the

looming disruption remains difficult to predict and will unfold unevenly, the burgeoning fields

of data science and AI attest to the fact that major changes have already taken place. In this

context, it is particularly important to investigate which groups are accessing these newly

created jobs that are set to be the well‐paid, prestigious and intellectually stimulating jobs of the

future.

According to Berman and Bourne (2015), the new data science and AI professions

potentially offer a rare opportunity to disrupt the traditionally male‐dominated fields of

computing and engineering, narrow the gender gap in science, technology, engineering and

mathematics (STEM) and make diversity a priority early on. Yet the absence of women in the

fast‐growing AI and data science fields is already evident. Women make up 32% of workers in

AI and data roles worldwide (World Economic Forum, 2021), and only 18% of users across the

largest online global data science platforms (Young et al., 2021). Loukides' (2021) data/AI salary

survey found that women's data science salaries are already significantly lower than men's,

equating to 84% of the average salary for men regardless of education or job title. Despite these

top‐level estimations, however, there is a distinct scarcity of quality, disaggregated data on

women in data and AI. This is essential to interrogate and tackle inequities in the AI and data

science workforce (Zhang et al., 2021), and it is particularly difficult to make gender gaps in

industry visible without granular data. Such gender data gaps are not only the result of this data

not being made public, or even collected in the first place, but also due to the nascent—and

crucially fast‐moving and rapidly growing—nature of these fields and professions

(Dorschel, 2021). In this paper, we analyse a new cross‐country data set using innovative

data science methodology, including creative data curation and novel classification methods, to

explore the gendered dynamics of data and AI careers. A key contribution of this paper is to

show the depth of exploration into gendered careers in AI which is possible when detailed data

is made available.

Feminist scholars have long pointed out the ways in which labour markets are shaped by

gender relations and the key role that the historical association of technical skills with

masculinity has played. The under‐representation of women in STEM education and

professions is well documented (Blackburn, 2017). To date, however, research on the gender

segregation of technical occupations typically examines the broad information and

communication technology (ICT) sector (e.g., Segovia‐Pérez et al., 2019). Our work goes

beyond such studies, specifically honing in on the newly emerging professions of data science

and AI, about which little is known (Dorschel, 2021). These elite occupations not only confer

social and economic capital, but are also in the vanguard of determining how the latest

technologies will be designed. As such it is especially apposite to examine the extent to which

the link between gender and technical expertise is being disrupted or reproduced in these

nascent fields. As Howcroft and Rubery (2019) note, if the women who do succeed in entering

tech are stratified into ‘less prestigious’subfields and specialties, rather than obtaining those

jobs at the forefront of technical innovation, gender gaps in the future world of work, including

the gender pay gap, will be widened.

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