Nearly 20 years ago, Gaver et al. introduced ambiguity as a design resource, proposing tactics to reflect everyday uncertainty into interactive systems. This approach is especially relevant for self-tracking wearables, which often obscure the inherent ambiguity of system design and tracked phenomena with seemingly clear, prescriptive data and insights. Although scholars recognize the importance of ambiguity, its practical application in the design process remains underexplored. To address this, we conducted a two-week workshop with 60 designers, examining the application of Gaver et al.'s tactics into 11 design concepts, and performed interviews with 16 participants. Our findings reveal eight relevant ambiguity tactics for self-tracking and offer insights into participants' experiences with designing using ambiguity. We discuss prescription and overlooked ambiguity as levers for the operationalization of ambiguity, the potential benefits and downsides of ambiguity tactics for users, future directions for HCI research and practice, and the study limitations.

Generative Artificial Intelligence (GenAI) text-to-image models have made significant progress in emulating human-like outputs. However, understanding the inner functioning of these models remains a challenge due to their complexity and black-box nature. It has been observed that individuals naturally develop informal conceptualizations, termed “folk theories,” to explain the behaviors of algorithmic systems. The specific nature of GenAI text-to-image models, which are obscure in their working principles, yet carry out activities that are peculiar to humans, makes it interesting to investigate people’s theorization about this technology. With this aim, we conducted a qualitative interview study with 20 participants and observed how they accounted for the outputs of Stable Diffusion. The study findings show that participants developed a wide spectrum of conceptualizations, including folk theories that appear distinctive of GenAI text-to-image technology, also ascribing to the model a variety of “mental states.” Furthermore, we found that theory building follows different inductive and deductive trajectories, with participants employing diverse strategies to explain the functioning of the technology.

• We conducted a qualitative study on how people perceive LLMs’ unpredictable behaviors • We interviewed 20 participants to gather their feedback on a hallucination dialogue • We found that unpredictable behaviors change how people experience ChatGPT • We show that these behaviors evoke unsettling emotions and fear of AI Large Language Models (LLMs) have shown impressive capabilities in producing texts of quality and fluency that are similar to those created by humans. Despite their increasing use, however, the broader population's experience of many aspects of interaction with LLMs remains underexplored. This study investigates how diverse individuals perceive and account for “nonsensical hallucinations”, namely, an LLM's unpredictable and meaningless behavior provided as a response to a user's request. We asked 20 participants to interact with ChatGPT 3.5 and experience its hallucinations. Through semi-structured interviews, we found that participants with a computer science background or consistent previous use of LLMs interpret unpredictable nonsensical responses as an error, while novices perceive them as model's autonomous behaviors. Moreover, we discovered that such responses produce an abrupt modification of participants’ perceptions and understandings of the LLM's nature. From a soothing and polite entity, ChatGPT becomes either an obscure and unfamiliar “alien”, or a human-like being potentially hostile to humankind, making also emerge unsettling feelings, which may unveil an underlying fear of Artificial Intelligence. The study contributes to literature on how people react to the unfamiliarity of a technology that may be perceived as alien and yet extremely human-like, generating “uncanny effects,” as well as to research on the anthropomorphizing of technology.

Generative Artificial Intelligence (Gen-AI) has rapidly advanced in recent years, potentially producing enormous impacts on industries, societies, and individuals in the near future. In particular, Gen-AI text-to-image models allow people to easily create high-quality images possibly revolutionizing human creative practices. Despite their increasing use, however, the broader population's perceptions and understandings of Gen-AI-generated images remain understudied in the Human-Computer Interaction (HCI) community. This study investigates how individuals, including those unfamiliar with Gen-AI, perceive Gen-AI text-to-image (Stable Diffusion) outputs. Study findings reveal that participants appraise Gen-AI images based on their technical quality and fidelity in representing a subject, often experiencing them as either prototypical or strange: these experiences may raise awareness of societal biases and evoke unsettling feelings that extend to the Gen-AI itself. The study also uncovers several “relational” strategies that participants employ to cope with concerns related to Gen-AI, contributing to the understanding of reactions to uncanny technology and the (de)humanization of intelligent agents. Moreover, the study offers design suggestions on how to use the anthropomorphizing of the text-to-image model as design material, and the Gen-AI images as support for critical design sessions.