Work

Generative AI and the Allocation of Administrative Burden in Public Employment Services for Older Jobseekers

Year: 2026

Type: article

Abstract: Generative AI can simplify access to public employment guidance while asking applicants to verify the answers they receive. When verification requires knowledge of official rules or system operation, the work assigned to applicants may exceed what they can reasonably perform. This article examines how that work should be allocated in public employment services for older jobseekers. Focusing on registration and vocational training guidance, we use conceptual analysis and normative argument to distinguish confirmation of personal information, verification of official rule interpretations, and diagnosis of system errors. We assess their allocation against purpose and necessity, relevant information and control, practical capacity and available assistance, and the consequences and reversibility of error. Applicants can reasonably confirm readily known facts when the effort is proportionate and mistakes are easy to correct. Primary responsibility for interpreting rules and diagnosing failures should remain with providers who possess the relevant knowledge, authority and control. Assistance should respond to applicants’ actual resources and preserve their choice to proceed independently or seek support. Institutional accountability extends to the initial enquiry, where erroneous guidance can prevent an application, and to timely correction that preserves the opportunity to proceed. The analysis establishes a basis for distinguishing reasonable citizen participation from the transfer of institutional work to applicants, and for organizing accessible assistance and effective correction in AI-assisted public services.

Source: Transformative Society

DOI: 10.63336/transsoc.61