Johannes Schöning

Nick von Felten, Luisa Ella Müller, Johannes Schöning

Expectations about the support of artificial intelligence (AI) may influence interaction outcomes similar to placebos. Such expectations may result from AI washing, a practice of overstating a system's AI capabilities when actual functionality is limited. For example, some computer mice are marketed as "AI-assisted" despite lacking AI in core functions. In a within-subjects study, 28 participants completed Fitts' Law tasks with a computer mouse under three conditions: no support, supposed predictive AI support, and supposed biosignal-enhanced AI support. Objective Fitts' Law performance indicators and subjective performance expectations, perceived workload, and perceived usability were measured. Compared to baseline, participants expected significantly improved performance in placebo conditions. However, these expectations did not translate into differences in objective or subjective assessments. This paper contributes evidence that AI washing inflates user expectations without altering actual interaction outcomes, highlighting a critical transparency issue. By exposing how deceptive AI marketing can shape user expectations, we underscore the need for accountability in AI product claims. Further, we establish Fitts' Law as a rigorous methodological lens for auditing AI-labelled input devices.

Nick von Felten, Johannes Schöning, Klaus Opwis et al.

Humans increasingly interact with artificial intelligence (AI) in decision-making. However, both AI and humans are prone to biases. While AI and human biases have been studied extensively in isolation, this paper examines their complex interaction. Specifically, we examined how class imbalance as an AI bias affects people's ability to appropriately rely on an AI-based decision-support system, and how it interacts with base rate neglect as a human bias. In a within-subject online study (N= 46), participants classified three diseases using an AI-based decision-support system trained on either a balanced or unbalanced dataset. We found that class imbalance disrupted participants' calibration of AI reliance. Moreover, we observed mutually reinforcing effects between class imbalance and base rate neglect, offering evidence of a compound human-AI bias. Based on these findings, we advocate for an interactionist perspective and further research into the mutually reinforcing effects of biases in human-AI interaction.

Daniel Diethei, Ashley Colley, Julian Wienert et al.

Online health communities provide a knowledge exchange platform for a wide range of diseases and health conditions. Informational and emotional support helps forum participants orient around health issues beyond in-person doctor visits. So far, little is known about the relation between the level of participation and participants' contributions in online health communities. To gain insights on the issue, we analyzed 456 posts in 56 threads from the Dermatology sub-forum of an online health community. While low participation threads (short threads) revolved around solving an individual's health issue through diagnosis suggestions and medical advice, participants in high participation threads (long threads) built collective knowledge and a sense of community, typically discussing chronic and rare conditions that medical professionals were unfamiliar with or could not treat effectively. Our results suggest that in short threads an individual's health issue is addressed, while in long threads, sub-communities about specific rare and chronic diseases emerge. This has implications for the user interface design of health forums, which could be developed to better support community building elements, even in short threads.

Daniel Diethei, Ashley Colley, Lisa Dannenberg et al.

The majority of blindness is preventable, and is located in developing countries. While mHealth applications for retinal imaging in combination with affordable smartphone lens adaptors are a step towards better eye care access, the expert knowledge and additional hardware needed are often unavailable in developing countries. Eye screening apps without lens adaptors exist, but we do not know much about the experience of guiding users to take medical eye images. Additionally, when an AI based diagnosis is provided, trust plays an important role in ensuring in the adoption. This work addresses factors that impact the usability and trustworthiness dimensions of mHealth applications. We present the design, development and evaluation of EyeGuide, a mobile app that assists users in taking medical eye images using only their smartphone camera. In a study (n=28) we observed that users of an interactive tutorial captured images faster compared to audible tone based guidance. In a second study (n=40) we found out that providing disease-specific background information was the most effective factor to increase trustworthiness in the AI based diagnosis. Application areas of EyeGuide are AI based disease detection and telemedicine examinations.

Ankit Kariryaa, Tony Veale, Johannes Schöning

A significant amount of our daily lives is dedicated to driving, leading to an unavoidable exposure to driving-related stress. The rise of autonomous vehicles will likely lessen the extent of this stress and enhance the routine traveling experience. Yet, no matter how diverse they may be, current routing criteria are limited to considering only the passive preferences of a vehicle's users. Thus, to enhance the overall driving experience in autonomous vehicles, we advocate here for the diversification of routing criteria, by additionally emphasizing activity- and mood-based requirements.

Isaac Johnson, Allen Yilun Lin, Toby Jia-Jun Li et al.

Wikipedia articles about places, OpenStreetMap features, and other forms of peer-produced content have become critical sources of geographic knowledge for humans and intelligent technologies. In this paper, we explore the effectiveness of the peer production model across the rural/urban divide, a divide that has been shown to be an important factor in many online social systems. We find that in both Wikipedia and OpenStreetMap, peer-produced content about rural areas is of systematically lower quality, is less likely to have been produced by contributors who focus on the local area, and is more likely to have been generated by automated software agents (i.e. bots). We then codify the systemic challenges inherent to characterizing rural phenomena through peer production and discuss potential solutions.

Ashley Colley, Jacob Thebault-Spieker, Allen Yilun Lin et al.

The widespread popularity of Pokémon GO presents the first opportunity to observe the geographic effects of location-based gaming at scale. This paper reports the results of a mixed methods study of the geography of Pokémon GO that includes a five-country field survey of 375 Pokémon GO players and a large scale geostatistical analysis of game elements. Focusing on the key geographic themes of places and movement, we find that the design of Pokémon GO reinforces existing geographically-linked biases (e.g. the game advantages urban areas and neighborhoods with smaller minority populations), that Pokémon GO may have instigated a relatively rare large-scale shift in global human mobility patterns, and that Pokémon GO has geographically-linked safety risks, but not those typically emphasized by the media. Our results point to geographic design implications for future systems in this space such as a means through which the geographic biases present in Pokémon GO may be counteracted.