\\\{Labeling is critical in creating training datasets for supervised machine learning, and is a common form of crowd work heteromation. It typically requires manual labor, is badly compensated and not infrequently bores the workers involved. Although task variety is known to drive human autonomy and intrinsic motivation, there is little research in this regard in the labeling context. Against this backdrop, we manipulate the presentation sequence of a labeling task in an online experiment and use the theoretical lens of self-determination theory to explain psychological work outcomes and work performance. We rely on 176 crowd workers contributing with group comparisons between three presentation sequences (by label, by image, random) and a mediation path analysis along the phenomena studied. Surprising among our key findings is that the task variety when sorting by label is perceived higher than when sorting by image and the random group. Naturally, one would assume that the random group would be perceived as most varied. We choose a visual metaphor to explain this phenomenon, whereas paintings offer a structured presentation of coloured pixels, as opposed to random noise.}

In Explainable Artificial Intelligence (XAI) research, various local model-agnostic methods have been proposed to explain individual predictions to users in order to increase the transparency of the underlying Artificial Intelligence (AI) systems. However, the user perspective has received less attention in XAI research, leading to a (1) lack of involvement of users in the design process of local model-agnostic explanations representations and (2) a limited understanding of how users visually attend them. Against this backdrop, we refined representations of local explanations from four well-established model-agnostic XAI methods in an iterative design process with users. Moreover, we evaluated the refined explanation representations in a laboratory experiment using eye-tracking technology as well as self-reports and interviews. Our results show that users do not necessarily prefer simple explanations and that their individual characteristics, such as gender and previous experience with AI systems, strongly influence their preferences. In addition, users find that some explanations are only useful in certain scenarios making the selection of an appropriate explanation highly dependent on context. With our work, we contribute to ongoing research to improve transparency in AI.

In recent years, new types of interactive analytical dashboard features have emerged for operational decision support systems (DSS). Analytical components of such features solve optimization problems hidden from the human eye, whereas interactive components involve the individual in the optimization process via graphical user interfaces (GUIs). Despite their expected value for organizations, little is known about the effectiveness of interactive analytical dashboards in operational DSS or their influences on human cognitive abilities. This paper contributes to the closing of this gap by exploring and empirically testing the effects of interactive analytical dashboard features on situation awareness (SA) and task performance in operational DSS. Using the theoretical lens of SA, we develop hypotheses about the effects of a what-if analysis as an interactive analytical dashboard feature on operational decision-makers' SA and task performance. The resulting research model is studied with a laboratory experiment, including eye-tracking data of 83 participants. Our findings show that although a what-if analysis leads to higher task performance, it may also reduce SA, nourishing a potential out-of-the-loop problem. Thus, designers and users of interactive analytical dashboards have to carefully mitigate these effects in the implementation and application of operational DSS. In this article, we translate our findings into implications for designing dashboards within operational DSS to help practitioners in their efforts to address the danger of the out-of-the-loop syndrome.

In aircraft ground handling, ramp agents manage the teams on the airport apron who prepare aircraft for the next takeoff. We examined the impact of using auditory and visual notification cues (NC) in an information system for supporting ramp agents, measuring the users’ situation awareness (SA), task performance, and perceived ease of use. Eighty-eight participants took part in a laboratory experiment and performed a realistic dispatching task of a ramp agent. Results showed that auditory cues facilitate superior SA compared with the application of visual cues or their absence. Our findings may serve as a foundation for future work aiming to design more effective notification systems to support SA.