Trust between humans and AI in the context of decision-making has acquired an important role in public policy, research and industry. In this context, Human-AI Trust has often been tackled from the lens of cognitive science and psychology, but lacks insights from the stakeholders involved. In this paper, we conducted semi-structured interviews with 7 AI practitioners and 7 decision subjects from various decision domains. We found that 1) interviewees identified the prerequisites for the existence of trust and distinguish trust from trustworthiness, reliance, and compliance; 2) trust in AI-integrated systems is strongly influenced by other human actors, more than the system’s features; 3) the role of Human-AI trust factors is stakeholder-dependent. These results provide clues for the design of Human-AI interactions in which trust plays a major role, as well as outline new research directions in Human-AI Trust.

On online platforms, algorithms help us build and manage our relationships. However, their invisible interventions might also pose harm to these connections. Dating platforms offer a prime example where, despite extensive research on human-inflicted harm, the potential harm from the algorithms themselves, and user strategies for mitigating them, remains largely unexplored. In our analysis of 7,043 reviews and interviews with 30 Tinder users, we unveiled how users perceive algorithmic harm as damaging self-esteem, sabotaging potential relationships, encouraging antisocial behavior, and misrepresenting or marginalizing certain identities. We introduce a new algorithmic folk theory, the "conflict of interest" theory, perceived to perpetuate these harms. This theory encapsulates users' sense of a contradiction between the dating platform's promise of finding the perfect partner (leading to discontinued use of Tinder) and its commercial interest in retaining users to increase revenue. Users suspected various algorithmic processes pursuant to this theory, such as (a) throttling profile visibility, (b) manipulating users' matches, and (c) recommending large quantities of profiles that will not lead to matches. They also described various strategies in resistance or defense of these suspected algorithmic processes, such as engaging in counter-intuitive behaviour to disrupt the unfavorable algorithmic processes or leveraging location based filtering for match variety and safety. We conclude by discussing how the perceived algorithmic harms can inform the development of new algorithmic implementations that balance both user and company interests.

Much is known about social engineering strategies (SE) during the attack phase, but little is known about the post-attack period. To address this gap, we conducted 17 narrative interviews with victims of cyber fraud. We found that while it was seen to be important for victims to act immediately and to take countermeasures against attack, they often did not do so. In this paper, we describe this "delay" in victims' responses as entailing a period of doubt and trust in good faith. The delay in victim response is a direct consequence of various SE techniques, such as exploiting prosocial behavior with subsequent negative effects on emotional state and interpersonal relationships. Our findings contribute to shaping digital resistance by helping people identify and overcome delay techniques to combat their inaction and paralysis.

Recently, researchers have proposed to develop automated self-balancing functions for bicycles to increase road safety and convenience. However, no study has investigated how self-balancing bicycles are perceived by potential users in a natural urban environment. Therefore, we conducted a field study using a modified “parent-child tandem” in which both the front and back seat passengers could share control of the riding dynamics. An experimenter in the back seat acted as an automated system, and participants in the front seat experienced the ride while responding to text messages on their smartphones. Based on interviews, video observation, and eye-tracking data, the results highlight potential use cases for self-balancing bicycles and uncover that trust and multitasking freedom can lead to similar problems as in automated cars.

In both data protection law and research of usable privacy, awareness and control over the collection and use of personal data are understood to be cornerstones of digital sovereignty. For example, the European General Data Protection Regulation (GDPR) provides data subjects with the right to access data collected by organisations but remains unclear on the concrete process design. However, the design of data subject rights is crucial when it comes to the ability of customers to exercise their right and fulfil regulatory aims such as transparency. To learn more about user needs in implementing the right to access as per GDPR, we conducted a two-step study. First, we defined a five-phase user experience journey regarding the right to access: finding, authentication, request, access and data use. Second, and based on this model, 59 participants exercised their right to access and evaluated the usability of each phase. Drawing on 422 datasets spanning 139 organisations, our results show several interdependencies of process design and user satisfaction. Thereby, our insights inform the community of usable privacy and especially the design of the right to access with a first, yet robust, empirical body.

Voice assistants (VA) collect data about users’ daily life including interactions with other connected devices, musical preferences, and unintended interactions. While users appreciate the convenience of VAs, their understanding and expectations of data collection by vendors are often vague and incomplete. By making the collected data explorable for consumers, our research-through-design approach seeks to unveil design resources for fostering data literacy and help users in making better informed decisions regarding their use of VAs. In this paper, we present the design of an interactive prototype that visualizes the conversations with VAs on a timeline and provides end users with basic means to engage with data, for instance allowing for filtering and categorization. Based on an evaluation with eleven households, our paper provides insights on how users reflect upon their data trails and presents design guidelines for supporting data literacy of consumers in the context of VAs.