Obtaining meaningful user consent is increasingly problematic in a world of numerous, heterogeneous digital services. Current approaches (e.g. agreeing to Terms and Conditions) are rooted in the idea of individual control despite growing evidence that users do not (or cannot) exercise such control in informed ways. We consider an alternative approach whereby users can opt to delegate consent decisions to an ecosystem of third-parties including friends, experts, groups and AI entities. We present the results of a study that used a technology probe at a large festival to explore initial public responses to this reframing -- focusing on when and to whom users would delegate such decisions. The results reveal substantial public interest in delegating consent and identify differing preferences depending on the privacy context, highlighting the need for alternative decision mechanisms beyond the current focus on individual choice.

Computational systems and objects are becoming increasingly closely integrated with our daily activities. Ubiquitous and pervasive computing first identified the emerging challenges of studying technology used on-the-move and in widely varied contexts. With IoT, previously sporadic experiences are interconnected across time and space in numerous and complex ways. This increasing complexity has multiplied the challenges facing those who study human experience to inform design. This paper describes the results of a study that used a chatbot or 'Ethnobot' to gather ethnographic data, and considers the opportunities and challenges in collecting this data in the absence of a human ethnographer. This study involved 13 participants gathering information about their experiences at the Royal Highland Show. We demonstrate the effectiveness of the Ethnobot in this setting, discuss the benefits and drawbacks of chatbots as a tool for ethnographic data collection, and conclude with recommendations for the design of chatbots for this purpose.

Technologies such as distributed ledgers and smart contracts are enabling the emergence of new autonomous systems, and providing enhanced systems to track the provenance of goods. A growing body of work in HCI is exploring the novel challenges of these systems, but there has been little attention paid to their impact on everyday activities. This paper presents a study carried out in 3 office environments for a 1-month period, which explored the impact of an autonomous coffee machine on the everyday activity of coffee consumption. The Bitbarista mediates coffee consumption through autonomous processes, presenting provenance data at the time of purchase while attempting to reduce intermediaries in the coffee trade. Through the report of interactions with and around the Bitbarista, we explore its implications for everyday life, and wider social structures and values. We conclude by offering recommendations for the design of community shared autonomous systems.

We are surrounded by a proliferation of connected devices performing increasingly complex data transactions. Traditional design methods tend to simplify or conceal this complexity to improve ease of use. However, the hidden nature of data is causing increasing discomfort. This paper presents BitBarista, a coffee machine designed to explore perceptions of data processes in the Internet of Things. BitBarista reveals social, environmental, qualitative and economic aspects of coffee supply chains. It allows people to choose a source of future coffee beans, situating their choices within the pool of decisions previously made. In doing so, it attempts to engage them in the transactions that are required to produce coffee. Initial studies of BitBarista with 42 participants reveal challenges of designing for connected systems, particularly in terms of perceptions of data gathering and sharing, as well as assumptions generated by current models of consumption. A discussion is followed by a series of suggestions for increasing positive attitudes towards data use in interactive systems.