Project aims to tackle gender/ethnic bias in AI recruitment

Researchers have set their sights on tackling the problem of gender and ethnic bias in recruitment and human resource management as part of a new £1m project.

The BIAS – Responsible AI for Labour Market Equality project will explore 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. Funding of £987k comes from 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 of Canada (NSERC); and the Social Sciences and Humanities Research Council of Canada (SSHRC).

Researchers will work with industrial partners to understand gender and ethnic bias within HR processes, such as hiring and professional networking, analysing data from across hiring and recruitment platforms and developing new tools and protocols to mitigate and address such bias. The aim is to enable companies, HR departments and recruitment agencies to tackle bias 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 project as principal investigator, working with Lancaster colleagues Dr Yang Hu, from Sociology, and Dr Bran Knowles, from the School of Computing and Communications.

The research ties in with the UK’s Industrial Strategy of ‘putting the UK at the forefront of the AI and data revolution’ as one of its main 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 links with the Canadian SSHRC’s goal of tackling persistent ethnic and gender disparities in workforce selection and development.

The project will also 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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