Previous research in Explainable Artificial Intelligence (XAI) suggests that a main aim of explainability approaches is to satisfy specific interests, goals, expectations, needs, and demands regarding artificial systems (we call these 'stakeholders' desiderata') in a variety of contexts. However, the literature on XAI is vast, spreads out across multiple largely disconnected disciplines, and it often remains unclear how explainability approaches are supposed to achieve the goal of satisfying stakeholders' desiderata. This paper discusses the main classes of stakeholders calling for explainability of artificial systems and reviews their desiderata.

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We introduce the concept of sensor-based applications for the daily business settings of organizations and their individual workers. Wearable sensor devices were developed and deployed in a real organization, a bank, for a month in order to study the effectiveness and potential of using sensors at the organizational level. It was found that patterns of physical interaction changed dynamically while e-mail is more stable from day to day. Different patterns of behavior between people in different rooms and teams (p < 0.01), as well as correlations between communication and a worker's subjective productivity, were also identified. By analyzing a fluctuation of network parameters, i.e., "betweenness centrality," it was also found that communication patterns of people are different: some people tend to communicate with the same people in regular frequency (which is hypothesized as a typical pattern of throughput-oriented jobs) while some others drastically changed their communication day by day (which is hypothesized as a pattern of creative jobs). Based on these hypotheses, a reorganization, such that people having similar characteristics work together, was proposed and implemented.

Abstract. In this project we aim to analyze "honest signals" between Jazz musicians by using sociometric badges with the goal of identifying some of the pre-requisites for "flow", the state of work where "time flies", and the worker is at his most-productive best. We extend the concept of individual "flow" as defined by Csikszentmihalyi (1990) to the group level, trying to identify some of the conditions indicative of the group flow state. We speculate that a band of Jazz musicians is particularly well suited to study group flow, because they collaborate as a self-organizing team, involved in highly creatively work while passing leadership of the tune for the solo part from one band member to the next.

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This paper describes first results of an ongoing research effort using real time data collected by social badges to correlate temporal changes in social interaction patterns with performance of individual actors and groups. Towards that goal we analyzed social interaction among a team of employees at a bank in Germany, and developed a set of interventions for more efficient collaboration. In particular, we were able to identify typical meeting patterns, and to distinguish between creative and high-executing knowledge work based on the interaction pattern.

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