Large language models (LLMs) can provide users with false, inaccurate, or misleading information, and we consider the output of this type of information as what Natale calls ‘banal’ deceptive behaviour [53]. Here, we investigate peoples’ perceptions of ChatGPT-generated deceptive behaviour and how this affects people’s behaviour and trust. To do this, we use a mixed-methods approach comprising of (i) an online survey with 220 participants and (ii) semi-structured interviews with 12 participants. Our results show that (i) the most common types of deceptive information encountered were over-simplifications and outdated information; (ii) humans’ perceptions of trust and chat-worthiness of ChatGPT are impacted by ‘banal’ deceptive behaviour; (iii) the perceived responsibility for deception is influenced by education level and the perceived frequency of deceptive information; and (iv) users become more cautious after encountering deceptive information, but they come to trust the technology more when they identify advantages of using it. Our findings contribute to understanding human-AI interaction dynamics in the context of Deceptive AI Ecosystems and highlight the importance of user-centric approaches to mitigating the potential harms of deceptive AI technologies.

Current privacy protections for smart home devices rarely consider bystanders' privacy, whose preferences are varied and may differ from primary users. We use Contextual Integrity theory to explore context-dependent variation in privacy norms regarding smart home bystanders’ data. We conducted a vignette-based survey with 761 participants in the US, varying parameter values to capture acceptability judgments regarding bystander information flows in certain situations: domestic work, shared housing, visiting a friend overnight, and Airbnb. We found that recipients and purposes of sharing impact acceptance the most. Sharing interaction logs was more acceptable than audio or video. Sharing smart speaker data was less acceptable than smart camera or smart door lock data. We found nuanced interaction effects between factors in different smart home situations, and differences between protections most favored by participants playing bystander vs. owner roles. We provide design and policy recommendations for smart home privacy protections that consider bystanders' needs.

AI assistants such as Alexa, Google Assistant, and Siri, are making their way into the healthcare sector, offering a convenient way for users to access different healthcare services. Trust is a vital factor in the uptake of healthcare services, but the factors affecting trust in voice assistants used for healthcare are under-explored and this specialist domain introduces additional requirements. This study explores the effects of different functional, personal, and risk factors on trust in and adoption of healthcare voice AI assistants (HVAs), generating a partial least squares structural model from a survey of 300 voice assistant users. Our results indicate that trust in HVAs can be significantly explained by functional factors (usefulness, content credibility, quality of service relative to a healthcare professional), together with security, and privacy risks and personal stance in technology. We also discuss differences in terms of trust between HVAs and general-purpose voice assistants as well as implications that are unique to HVAs.

As smart home devices proliferate, protecting the privacy of those who encounter the devices is of the utmost importance both within their own home and in other people's homes. In this study, we conducted a large-scale survey (N=1459) with primary users of and bystanders to smart home devices. While previous work has studied people's privacy experiences and preferences either as smart home primary users or as bystanders, there is a need for a deeper understanding of privacy experiences and preferences in different contexts and across different countries. Instead of classifying people as either primary users or bystanders, we surveyed the same participants across different contexts. We deployed our survey in four countries (Germany, Mexico, the United Kingdom, and the United States) and in two languages (English and Spanish). We found that participants were generally more concerned about devices in their own homes, but perceived video cameras—especially unknown ones—and usability as more concerning in other people's homes. Compared to male participants, female and non-binary participants had less control over configuration of devices and privacy settings—regardless of whether they were the most frequent user. Comparing countries, participants in Mexico were more likely to be comfortable with devices, but also more likely to take privacy precautions around them. We also make cross-contextual recommendations for device designers and policymakers, such as nudges to facilitate social interactions.

Smart Home Personal Assistants (SPA) have a complex ecosystem that enables them to carry out various tasks on behalf of the user with just voice commands. SPA capabilities are continually growing, with over a hundred thousand third-party skills in Amazon Alexa, covering several categories, from tasks within the home (e.g. managing smart devices) to tasks beyond the boundaries of the home (e.g. purchasing online, booking a ride). In the SPA ecosystem, information flows through several entities including SPA providers, third-party skills providers, providers of Smart Devices, other users and external parties. Prior studies have not explored privacy norms in the SPA ecosystem, i.e., the acceptability of these information flows. In this paper, we study privacy norms in SPAs based on Contextual Integrity through a large-scale study with 1,738 participants. We also study the influence that the Contextual Integrity parameters and personal factors have on the privacy norms. Further, we identify the similarities in terms of the Contextual Integrity parameters of the privacy norms studied to distill more general privacy norms, which could be useful, for instance, to establish suitable privacy defaults in SPA. We finally provide recommendations for SPA and third-party skill providers based on the privacy norms studied.