We present Dynamic Condition Response Graphs (DCR Graphs) as a declarative, event-based process model inspired by the workflow language employed by our industrial partner and conservatively generalizing prime event structures. A dynamic condition response graph is a directed graph with nodes representing the events that can happen and arrows representing four relations between events: condition, response, include, and exclude. Distributed DCR Graphs is then obtained by assigning roles to events and principals. We give a graphical notation inspired by related work by van der Aalst et al. We exemplify the use of distributed DCR Graphs on a simple workflow taken from a field study at a Danish hospital, pointing out their flexibility compared to imperative workflow models. Finally we provide a mapping from DCR Graphs to Büchi-automata.

Abstract. As part of ongoing work on evaluating Milner's bigraphical reactive systems, we investigate bigraphical models of context-aware systems, a facet of ubiquitous computing. We find that naively encoding such systems in bigraphs is somewhat awkward; and we propose a more sophisticated modeling technique, introducing Plato-graphical models, alleviating this awkwardness. We argue that such models are useful for simulation and point out that for reasoning about such bigraphical models, the bisimilarity inherent to bigraphical reactive systems is not enough in itself; an equivalence between the bigraphical reactive systems themselves is also needed.

No abstract

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.

No abstract