Beyond the Score: AI Hiring Algorithms, Vulnerable Groups, and the Future of Fair Work

Across the world, the decision to hire, promote, or let someone go is increasingly shaped by AI — the sociotechnical algorithms at the workplace are now embedded in application-tracking systems (ATS), predictive analytics, and automated assessment tools. These systems promise optimization and objectivity, yet a growing number of cases show how, when poorly designed, they can quietly reproduce disadvantage: ethnic minorities, migrants and refugees, people with disabilities and neurodivergent candidates, and many other vulnerable groups of workers may be misread, under-scored, or screened out — often because they were under-represented in the data these tools learned from.

This session turns its attention to the very diverse groups of people on the receiving end of the score. In the current turbulent time, when work is increasingly precarious, what happens to the life chances of vulnerable groups when an algorithm mediates them — not once, but across an entire career? What social researchers and other stakeholders can change for good?

We warmly invite empirical, theoretical, and applied contributions on the social consequences of algorithmic hiring and workplace assessment: long-term patterns of exclusion and precarity; intersectional disadvantage across class, age, gender, and migration background, and many other dimensions; the lived experiences of affected workers; and, above all, the best practices, fairer designs, and inclusive strategies that can help vulnerable people not just get hired, but belong. Researchers and practitioners from every region and tradition are invited to share their work — and to help imagine a more equitable future of work.