Algorithmic Decision-Making in Public Services: Two Empirical Cases of Algorithmic Incompetence

Algorithmic decision-making (ADM) is increasingly adopted in the public sector to support the delivery of social services. In this context, service workers play a critical role in ensuring service interactions remain human-centered, equitable, and responsive to the nuanced needs of each citizen. However, the nature of frontline work is being profoundly reshaped by ADM, along with the competencies required of service workers. This paper examines two public sector cases where ADM was introduced to facilitate service provision, but instead eroded critical competence and generated negative service experiences for many citizens. Drawing on perspectives from customer experience and employee mastery in service delivery, we examine how ADM disrupted service worker competence and the resulting implications for citizen experience. Our findings highlight the importance of preserving service workers' competence when designing and deploying ADM-driven public services.