Being unemployed is, by definition, someone without a job, but is it somebody without work? This paper presents an empirical study of unemployed job seekers and the work they do in connection with upholding their everyday life, job search, technology use, and the necessary connections with the public unemployment office (job center). Within the country where the research is conducted, the activities of the unemployed involve balancing formal legal requirements and procedures (to secure unemployment benefits, etc.) with informal navigation of the rules and regulations, networks and activities directed towards employment. The activities are mediated by digital platforms, e.g. job search, accountability, documentation, and, as a consequence, job seekers largely have to depend on their technologies and experiences to manage these processes. We present a study of interviews with job seekers (n=10) with an average age of 60, and two Participatory Design workshops with a total of 15 participants. Our analysis shows that the unemployed carry out work towards fulfilling formal requirements posed by the unemployment office, as well as setting up their daily, computer-supported routines, and a significant amount of unrecognized hidden work navigating rules, status and informal networks.

Studies of algorithmic decision-making in Computer-Supported Cooperative Work (CSCW) and related fields of research increasingly recognize an analogy between AI and bureaucracies. We elaborate this link with an empirical study of AI in the context of decision-making in a street-level bureaucracy: job placement. The study examines caseworkers' perspectives on the use of AI, and contributes to an understanding of bureaucratic decision-making, with implications for integrating AI in caseworker systems. We report findings from a participatory workshop on AI with 35 caseworkers from different types of public services, followed up by interviews with five caseworkers specializing in job placement. The paper contributes an understanding of caseworkers' collaboration around documentation as a key aspect of bureaucratic decision-making practices. The collaborative aspects of casework are important to show because they are subject to process descriptions making case documentation prone for an individually focused AI with consequences for the future of how casework develops as a practice. Examining the collaborative aspects of caseworkers' documentation practices in the context of AI and (potentially) automation, our data show that caseworkers perceive AI as valuable when it can support their work towards management, (strengthen their cause, if a case requires extra resources), and towards unemployed individuals (strengthen their cause in relation to the individual's case when deciding on, and assigning a specific job placement program). We end by discussing steps to support cooperative aspects in AI decision-support systems that are increasingly implemented into the bureaucratic context of public services.