As Facial Recognition Technologies (FRTs) are increasingly common in society, it is imperative to support the public in understanding and critiquing their use.We used an existing FRT application to generate characters for Dungeons and Dragons (D&D), a tabletop Role-Playing Game, based solely on an uploaded photograph of a player's face.Results from over 30,000 generated characters demonstrate that an FRT dataset is sufficient to generate simple D&D characters.However, it is essential to interrogate the underlying assumptions that feed into the generated characters and the value that this provides to player groups.This work highlights the biases that are embedded in FRT datasets, particularly in relation to assumptions relating to players' inferred gender.

Facial analysis applications are increasingly being applied to inform decision-making processes. However, as global reports of unfairness emerge, governments, academia and industry have recognized the ethical limitations and societal implications of this technology. Alongside initiatives that aim to formulate ethical frameworks, we believe that the public should be invited to participate in the debate. In this paper, we discuss Biometric Mirror, a case study that explored opinions about the ethics of an emerging technology. The interactive application distinguished demographic and psychometric information from people's facial photos and presented speculative scenarios with potential consequences based on their results. We analyzed the interactions with Biometric Mirror and media reports covering the study. Our findings demonstrate the nature of public opinion about the technology's possibilities, reliability, and privacy implications. Our study indicates an opportunity for case study-based digital ethics research, and we provide practical guidelines for designing future studies.

In HCI, the honeypot effect describes a form of audience engagement in which a person's interaction with a technology stimulates passers-by to observe, approach and engage in an interaction themselves. In this paper we explore the potential for honeypot effects to arise in the use of mobile augmented reality (AR) applications in urban spaces. We present an observational study of Santa's Lil Helper, a mobile AR game that created a Christmas-themed treasure hunt in a metropolitan area. Our study supports a consideration of three factors that may impede the honeypot effect: the presence of people in relation to the game and its interactive components; the visibility of gameplay in urban space; and the extent to which the game permits a shared experience. We consider how these factors can inform the design of future AR experiences that are capable of stimulating honeypot effects in public space.

In HCI, the honeypot effect describes how people interacting with a system passively stimulate passers-by to observe, approach and engage in an interaction. Previous research has revealed the successive engagement phases and zones of the honeypot effect. However, there is little insight into: 1) how people are stimulated to transition between phases; 2) what aspects drive the honeypot effect apart from watching others; and 3) what constraints affect its self-reinforcing performance. In this paper, we discuss the honeypot effect as a spatiotemporal model of trajectories and influences. We introduce the Honeypot Model based on the analysis of observations and interaction logs from Encounters, a public installation that interactively translated bodily movements into a dynamic visual and sonic output. In providing a model that describes trajectories and influences of audience engagement in public interactive systems, our paper seeks to inform researchers and designers to consider contextual, spatial and social factors that influence audience engagement.

In this paper, we investigate the potential of controlled in-the-wild studies as an evaluation methodology that merges the benefits of lab-based and in-the-wild studies. Our exploratory investigation builds upon a comparative, between subject experiment benchmarking different interaction features of a custom public installation that visualized a series of urban datasets. In order to evaluate the usefulness of the in-the-wild versus the controlled in-the-wild methodologies, we compared the resulting findings in terms of participant engagement, insight generation, and social interaction. We propose that a controlled in-the-wild study offers a viable alternative when evaluating more complex interaction methods in public space, hereby potentially reducing the practical efforts of in-the-wild studies to involve participants.