(in) Accuracy in Algorithmic Profiling of the Unemployed – An Exploratory Review of Reporting Standards
Patrick Gallagher, Ray Griffin
Abstract
Public Employment Services (PES) increasingly use automated statistical profiling algorithms (ASPAs) to ration expensive active labour market policy (ALMP) interventions to those they predict at risk of becoming long-term unemployed (LTU). Strikingly, despite the critical role played by ASPAs in the operation of public policy, we know very little about how the technology works, particularly how accurate predictions from ASPAs are. As a vital first step in assessing the operational effectiveness and social impact of ASPAs, we review the method of reporting accuracy. We demonstrate that the current method of reporting a single measure for accuracy (usually a percentage) inflates the capabilities of the technology in a peculiar way. ASPAs tend towards high false positive rates, and so falsely identify those who prove to be frictionally unemployed as likely to be LTU. This has important implications for the effectiveness of spending on ALMPs.