Using Artificial Intelligence to classify Jobseekers: The Accuracy-Equity Trade-off

Artificial intelligence (AI) is increasingly popular in the public sector to improve the cost-efficiency of service delivery. One example is AI-based profiling models in public employment services (PES), which predict a jobseeker's probability of finding work and are used to segment jobseekers in groups. Profiling models hold the potential to improve identification of jobseekers at-risk of becoming long-term unemployed, but also induce discrimination. Using a recently developed AI-based profiling model of the Flemish PES, we assess to what extent AI-based profiling 'discriminates' against jobseekers of foreign origin compared to traditional rule-based profiling approaches. At a maximum level of accuracy, jobseekers of foreign origin who ultimately find a job are 2.6 times more likely to be misclassified as 'high-risk' jobseekers. We argue that it is critical that policymakers and caseworkers understand the inherent trade-offs of profiling models, and consider the limitations when integrating these models in daily operations. We develop a graphical tool to visualize the accuracy-equity trade-off in order to facilitate policy discussions.

Automated summary

Artificial intelligence is increasingly used in public employment services to improve efficiency, but it can also lead to discrimination, especially against foreign jobseekers. Research shows that at maximum accuracy levels, jobseekers of foreign origin are 2.6 times more likely to be misclassified as 'high-risk' compared to local jobseekers. It is crucial for policymakers and caseworkers to understand the trade-offs of profiling models and consider their limitations in daily operations.