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Ludo Struyven

KU Leuven, Belgium

AI based profiling is replacing the judgment of human caseworkers. Automation is even used to suspend welfare payments without notice. But profiling may also enable a positive activation of jobseekers. Perhaps even more so than in the private sector, using profiling and algorithms to support or automate decision-making raises concerns about the fairness of algorithms

IZA World of Labor role

Author

Current position

Head of the Education and Labour Market Research Group at HIVA (Research Institute for Work and Society) and Professor in Sociology of Work and Education at the Faculty of Social Sciences (University of Leuven)

Research interest

The dynamics and transformations of labor markets, skills and degrees; activation policies, lifelong learning and employability; and the (re)positioning of public and private employment services

Website

https://hiva.kuleuven.be/en/stafflist/00014996

Qualifications

PhD in Social Sciences, KU Leuven

Selected publications

  • "Using artificial intelligence to classify jobseekers: The accuracy-equity trade-off." Journal Of Social Policy (2020) (with S. Desiere).

  • "Profiling tools for early identification of jobseekers who need extra support." OECD Policy Brief on Activation Policies (2018): 1–4 (with S. Desiere and K. Langenbucher).

  • "Interaction styles of street-level workers and motivation of clients: A new instrument to assess discretion-as-used in the case of activation of jobseekers." Public Management Review 20:11 (2018): 1702–1721 (with L. Van Parys).

  • "Smart policies or sheer luck? Labour market resilience in the low countries." Social Policy & Administration 48:4 (2014): 492–513 (with M. Fenger, F. Koster, and R. Van der Veen).

  • "Varieties of market competition in public employment services: A comparison of the emergence and evolution of the new system in Australia, the Netherlands and Belgium." Social Policy & Administration 48:2 (2014): 149–168.

article(s)

  • Statistical profiling of unemployed jobseekers

    The increasing availability of big data allows for the profiling of unemployed jobseekers via statistical models

    Bert van Landeghem Sam Desiere Ludo Struyven , February 2021
    Statistical models can help public employment services to identify factors associated with long-term unemployment and to identify at-risk groups. Such profiling models will likely become more prominent as increasing availability of big data combined with new machine learning techniques improve their predictive power. However, to achieve the best results, a continuous dialogue between data analysts, policymakers, and case workers is key. Indeed, when developing and implementing such tools, normative decisions are required. Profiling practices can misclassify many individuals, and they can reinforce but also prevent existing patterns of discrimination.
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