AI to help find causes of and reduce labour market gender and ethnic bias

Researchers will tackle the problem of gender and ethnic bias in recruitment and human resource management as part of a new £1m project

Researchers will tackle the problem of gender and ethnic bias in recruitment and human resource management as part of a new £1m project.

BIAS – Responsible AI for Labour Market Equality will look at how Artificial Intelligence can lead to unintentional bias in the processes of job advertising, hiring and professional networking, which are increasingly digitalised.

Lancaster University will lead the three-year project, working alongside Essex University and the University of Alberta. The UK Research and Innovation/Economic and Social Research Council (UKRI/ESRC); the Canadian Institutes of Health Research (CIHR); the Natural Sciences and Engineering Research Council (NSERC); and the Social Sciences and Humanities Research Council (SSHRC) have provided funding of £987,000.

The researchers will work with industrial partners to understand gender and ethnic bias within human resource processes, such as hiring and professional networking. They will analyse data from across hiring and recruitment platforms and develop new tools and protocols to mitigate and address such bias. This will allow companies, HR departments and recruitment agencies to tackle such issues in future recruitment.

Professor Monideepa Tarafdar, Professor of Information Systems and Co-Director of the Centre for Technological Futures at Lancaster University Management School, will lead the research as principal investigator, working with Lancaster colleagues Dr Yang Hu, from Sociology, and Dr Bran Knowles, from the School of Computing and Communications.

Professor Tarafdar said: “AI has reached the stage where the rubber is meeting the road and organisations are coming up against the road bumps. Bias is a huge one. We need to tackle labour market inequalities caused by gender and ethnic biases in hiring, job advertising and professional socialisation. They prevent equal and sustainable socio-economic development across all groups in society, and the recruitment process can often be the start of these issues. There are different causes and sources of this bias, and we want to investigate and mitigate them.

“In both the UK and Canada, access and rewards to work remain shaped around social distinctions, such as gender, race, and ethnicity, and the use of Artificial Intelligence is known to exacerbate such inequalities through a perpetuation of existing gender and ethnic biases in hiring and career progression.”

Dr Hu added: “We want to understand these biases and develop a tool that will mitigate against them. There is no way to remove all bias, and that would not be preferable – if I’m hiring somebody, I want there to be some variations that produces the best candidate – but we want to reduce it where we can and where it is optimal.”

The research ties in with the UK’s Industrial Strategy, which has ‘putting the UK at the forefront of the AI and data revolution’ as one of its grand challenges, as well as the UK’s AI sector deal that aims to ‘boost the UK’s global position as a leader in developing AI technologies’. It also speaks directly to the Canadian SSHRC’s goal of tackling persistent ethnic and gender disparities in workforce selection and development.

Dr Knowles said: “We believe that our research will allow businesses and recruitment agencies to better target potential employees without unintended bias that results in women or people from certain ethnic backgrounds from even applying for positions, let alone being recruited into them.”

The project will look to develop a protocol for responsible and trustworthy AI that reduces labour market inequalities by tackling gender and ethnic/racial biases in job advertising, hiring and professional networking processes.


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