Convolutional neural networks (CNNs) o er great machine learning performance over a range of applications, but their operation is hard to interpret, even for experts. Various explanation algorithms have been proposed to address this issue, yet limited research e ort has been reported concerning their user evaluation. In this paper, we report on an online between-group user study designed to evaluate the performance of "saliency maps" -a popular explanation algorithm for image classi cation applications of CNNs. Our results indicate that saliency maps produced by the LRP algorithm helped participants to learn about some speci c image features the system is sensitive to. However, the maps seem to provide very limited help for participants to anticipate the network's output for new images. Drawing on our ndings, we highlight implications for design and further research on explainable AI. In particular, we argue the HCI and AI communities should look beyond instance-level explanations.

The interpretation of data is fundamental to machine learning. This paper investigates practices of image data annotation as performed in industrial contexts. We define data annotation as a sense-making practice, where annotators assign meaning to data through the use of labels. Previous human-centered investigations have largely focused on annotators' subjectivity as a major cause of biased labels. We propose a wider view on this issue: guided by constructivist grounded theory, we conducted several weeks of fieldwork at two annotation companies. We analyzed which structures, power relations, and naturalized impositions shape the interpretation of data. Our results show that the work of annotators is profoundly informed by the interests, values, and priorities of other actors above their station. Arbitrary classifications are vertically imposed on annotators, and through them, on data. This imposition is largely naturalized. Assigning meaning to data is often presented as a technical matter. This paper shows it is, in fact, an exercise of power with multiple implications for individuals and society.CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing; • Social and professional topics → Employment issues; • Computing methodologies → Supervised learning by classification.

While whole body interaction can enrich user experience on public displays, it remains unclear how common visualizations of user representations impact users' ability to perceive content on the display. In this work we use a head-mounted eye tracker to record visual behavior of 25 users interacting with a public display game that uses a silhouette user representation, mirroring the users' movements. Results from visual attention analysis as well as post-hoc recall and recognition tasks on display contents reveal that visual attention is mostly on users' silhouette while peripheral screen elements remain largely unattended. In our experiment, content attached to the user representation attracted significantly more attention than other screen contents, while content placed at the top and bottom of the screen attracted significantly less. Screen contents attached to the user representation were also significantly better remembered than those at the top and bottom of the screen.

The multi-touch-based pinch to zoom, drag and flick to pan metaphor has gained wide popularity on mobile displays, where it is the paradigm of choice for navigating 2D documents. But is finger-based navigation really the gold standard? In this paper, we present a comprehensive user study with 40 participants, in which we systematically compare the Pinch-Drag-Flick approach with a technique that relies on spatial manipulation, such as lifting a display up/down to zoom. While we solely considered known techniques, we put considerable effort in implementing both input strategies on popular consumer hardware (iPhone, iPad). Our results show that spatial manipulation can significantly outperform traditional Pinch-Drag-Flick. Given the carefully optimized prototypes, we are confident to have found strong arguments that future generations of mobile devices could rely much more on spatial interaction principles.