Computer Science

A Bayesian view of data interpretation suggests that a visualization user should update their existing beliefs about a parameter's value in accordance with the amount of information about the parameter value captured by the new observations. Extending recent work applying Bayesian models to understand and evaluate belief updating from visualizations, we show how the predictions of Bayesian inference can be used to guide more rational belief updating. We design a Bayesian inference-assisted uncertainty analogy that numerically relates uncertainty in observed data to the user's subjective uncertainty, and a posterior visualization that prescribes how a user should update their beliefs given their prior beliefs and the observed data. In a pre-registered experiment on 4,800 people, we find that when a newly observed data sample is relatively small (N=158), both techniques reliably improve people's Bayesian updating on average compared to the current best practice of visualizing uncertainty in the observed data. For large data samples (N=5208), where people's updated beliefs tend to deviate more strongly from the prescriptions of a Bayesian model, we find evidence that the effectiveness of the two forms of Bayesian assistance may depend on people's proclivity toward trusting the source of the data. We discuss how our results provide insight into individual processes of belief updating and subjective uncertainty, and how understanding these aspects of interpretation paves the way for more sophisticated interactive visualizations for analysis and communication.

To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples stakeholders' knowledge from their interpretability needs. We characterize stakeholders by their formal, instrumental, and personal knowledge and how it manifests in the contexts of machine learning, the data domain, and the general milieu. We additionally distill a hierarchical typology of stakeholder needs that distinguishes higher-level domain goals from lower-level interpretability tasks. In assessing the descriptive, evaluative, and generative powers of our framework, we find our more nuanced treatment of stakeholders reveals gaps and opportunities in the interpretability literature, adds precision to the design and comparison of user studies, and facilitates a more reflexive approach to conducting this research.

Machines, from artificially intelligent digital assistants to embodied robots, are becoming more pervasive in everyday life. Drawing on feminist science and technology studies (STS) perspectives, we demonstrate how machine designers are not just crafting neutral objects, but relationships between machines and humans that are entangled in human social issues such as gender and power dynamics. Thus, in order to create a more ethical and just future, the dominant assumptions currently underpinning the design of these human-machine relations must be challenged and reoriented toward relations of justice and inclusivity. This paper contributes the "social machine" as a model for technology designers who seek to recognize the importance, diversity and complexity of the social in their work, and to engage with the agential power of machines. In our model, the social machine is imagined as a potentially equitable relationship partner that has agency and as an "other" that is distinct from, yet related to, humans, objects, and animals. We critically examine and contrast our model with tendencies in robotics that consider robots as tools, human companions, animals or creatures, and/or slaves. In doing so, we demonstrate ingrained dominant assumptions about human-machine relations and reveal the challenges of radical thinking in the social machine design space. Finally, we present two design challenges based on non-anthropomorphic figuration and mutuality, and call for experimentation, unlearning dominant tendencies, and reimagining of sociotechnical futures.

This paper addresses the question whether the recently proposed approach of concise privacy notices in apps and on websites is effective in raising user awareness. To assess the effectiveness in a realistic setting, we included concise notices in a fictitious but realistic fitness tracking app and asked participants recruited from an online panel to provide their feedback on the usability of the app as a cover story. Importantly, after giving feedback, users were also asked to recall the data practices described in the notices. The experimental setup included the variation of different levels of saliency and riskiness of the privacy notices. Based on a total sample of 2,274 participants, our findings indicate that concise privacy notices are indeed a promising approach to raise user awareness for privacy information when displayed in a salient way, especially in case the notices describe risky data practices. Our results may be helpful for regulators, user advocates and transparency-oriented companies in creating or enforcing better privacy transparency towards average users that do not read traditional privacy policies.

Deficit of attention, anxiety, sleep disorders are some of the problems which affect many persons. As these issues can evolve into severe conditions, more factors should be taken into consideration. The paper proposes a conception which aims to help students to enhance their brain performance. An electrocephalogram headset is used to trigger the brainwaves, along with a web application which manages the input data which comes from the headset and from the user. Factors like current activity, mood, focus, stress, relaxation, engagement, excitement and interest are provided in numerical format through the use of the headset. The users offer information about their activities related to relaxation, listening to music, watching a movie, and studying. Based on the analysis, it was found that the users consider the application easy to use. As the users are more equilibrated emotionally, their results are improved. This allowed the persons to be more confident on themselves. In the case of students, the neurofeedback can be studied for the better sport and artistic performances, including the case of the attention deficit hyperactivity disorder. Aptitudes for a subject can be determined based on the relevant generated brainwaves. The learning environment is an important factor during the analysis of the results. Teachers, professors, students and parents can collaborate and, based on the gathered data, new teaching methods can be adopted in the classroom and at home. The proposed solution can guide the students while studying, as well as the persons who wish to be more productive while solving their tasks.

We review how existing trends are relevant to the discussion of brain-computer interfaces and the data they would generate. Then, we posit how the commerce of neural data, dubbed Neurocapitalism, could be impacted by the maturation of brain-computer interface technology. We explore how this could pose fundamental changes to our way of interacting, as well as our sense of autonomy and identity. Because of the power inherent in the technology, and its potentially ruinous abuses, action must be taken before the appearance of the technology, and not come as a reaction to it. The widespread adoption of brain-computer interface technology will certainly change our way of life. Whether it is changed for the better or worse, depends on how well we prepare for its arrival.

Electroencephalography (EEG) signals elicited by multimodal stimuli can drive brain-computer interfaces (BCIs), and research has demonstrated that visual and auditory stimuli can be employed simultaneously to improve BCI performance. However, no studies have investigated the effect of multimodal stimuli in rapid serial visual presentation (RSVP) BCIs. In the present study, we propose a rapid serial multimodal presentation (RSMP) BCI that incorporates artificial facial images and artificial voice stimuli. To clarify the effect of audiovisual stimuli on the RSMP BCI, scrambled images and masked sounds were applied instead of visual and auditory stimuli, respectively. Our findings indicated that the audiovisual stimuli improved the performance of the RSMP BCI, and that the P300 at Pz contributed to classification accuracy. Online accuracy of BCI reached 85.7+-11.5%. Taken together, these findings may aid in the development of better gaze-independent BCI systems.

Virtual Reality (VR) has the potential to transform knowledge work. One advantage of VR knowledge work is that it allows extending 2D displays into the third dimension, enabling new operations, such as selecting overlapping objects or displaying additional layers of information. On the other hand, mobile knowledge workers often work on established mobile devices, such as tablets, limiting interaction with those devices to a small input space. This challenge of a constrained input space is intensified in situations when VR knowledge work is situated in cramped environments, such as airplanes and touchdown spaces. In this paper, we investigate the feasibility of interacting jointly between an immersive VR head-mounted display and a tablet within the context of knowledge work. Specifically, we 1) design, implement and study how to interact with information that reaches beyond a single physical touchscreen in VR; 2) design and evaluate a set of interaction concepts; and 3) build example applications and gather user feedback on those applications.

The more people commute by bicycle, the higher is the number of cyclists using their smartphones while cycling and compromising traffic safety. We have designed, implemented and evaluated two prototypes for smartphone control devices that do not require the cyclists to remove their hands from the handlebars - the three-button device Tribike and the rotation-controlled Brotate. The devices were the result of a user-centred design process where we identified the key features needed for a on-bike smartphone control device. We evaluated the devices in a biking exercise with 19 participants, where users completed a series of common smartphone tasks. The study showed that Brotate allowed for significantly more lateral control of the bicycle and both devices reduced the cognitive load required to use the smartphone. Our work contributes insights into designing interfaces for cycling.

There are a variety of graphs where multidimensional feature values are assigned to the nodes. Visualization of such datasets is not an easy task since they are complex and often huge. Immersive Analytics is a powerful approach to support the interactive exploration of such large and complex data. Many recent studies on graph visualization have applied immersive analytics frameworks. However, there have been few studies on immersive analytics for visualization of multidimensional attributes associated with the input graphs. This paper presents a new immersive analytics system that supports the interactive exploration of multidimensional feature values assigned to the nodes of input graphs. The presented system displays label-axes corresponding to the dimensions of feature values, and label-edges that connect label-axes and corresponding to the nodes. The system supports brushing operations which controls the display of edges that connect a label-axis and nodes of the graph. This paper introduces visualization examples with a graph dataset of Twitter users and reviews by experts on graph data analysis.