BIAS: Responsible AI for Labour Market Equality

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What do we study? BIAS is an interdisciplinary project to understand and tackle the role of AI algorithms in shaping ethnic and gender inequalities in the labour market, which is now increasingly digitized. The project seeks to understand and minimise gender and ethnic biases in the AI-driven labour market processes of job advertising, hiring and professional networking. We further aim to develop 'responsible' AI that mitigates biases and attendant inequalities, by designing AI algorithms and development protocols that are sensitive to such biases. The empirical context of our investigation includes these labour market processes in organisations and on digital job platforms.Why is it important? Labour market inequalities need to be tackled because they deny and thwart equitable and sustainable socio-economic development. In both the UK and Canada, access and rewards to work remain patterned around social distinctions, like gender, race, and ethnicity. The deployment of AI in labour market processes is known to exacerbate such inequalities through perpetuation of existing gender and ethnic biases hiring and career progression. From the point of view of policy our proposal speaks directly to the following priorities in both countries-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; the UK's AI sector deal that aims to 'boost the UK's global position as a leader in developing AI technologies'; and the Canadian SSHRC's goal of tackling persistent demographic (ethnic and gender) disparities in workforce selection and development.Why is it unique? Although we know that AI can exacerbate biases in the labour market, we do not know how AI can mitigate these biases. The project develops responsible and trustworthy AI that reduces labour market inequalities by tackling gender and ethnic/racial biases in job advertising, hiring and professional networking processes. It enhances capacity development for responsible AI applications through training of early career researchers, builds on existing and develops new UK-Canada research partnerships, and develops outputs for multiple stakeholders (researchers, companies and policy units). It speaks to multiple objectives of the funding call.Why is it intellectually original and challenging? The project is interdisciplinary. It integrates and cuts across three distinct streams of research (the first two are from the social sciences and the third from the computational and mathematical sciences) to tackle the research objective through an interdisciplinary approach. The first includes studies on socio-economic antecedents of labour market inequality, which underscore the persistence and prominence of gender and ethnic/racial inequalities in the UK and Canada. The second includes studies from business management (digitalisation, technology/AI adoption and human resource management). It emphasizes that while we know that the use of AI in the labour market processes of job advertising, hiring and professional networking can strengthen ethnic and gender bias in these processes we do not know what those biases are and how AI algorithms can mitigate them, as opposed to merely (re)producing them. The third draws on computational statistics (Bayesian statistic/machine learning) to design new AI algorithms and development protocols that integrate human and machine inputs/outputs.What is the work plan? Our project comprises two interlinked work packages that respectively (1) understand the different dimensions of bias from a multi-stakeholder perspective (e.g. employer, employee, digital platform developer) through in-depth data mining and qualitative investigations when AI algorithms are used in the labour market processes of job advertising, hiring and professional networking; and (2) test/design new AI algorithms to mitigate them and create protocols for their development and implementation.