This paper outlines the IoT Databox model as a means of making the Internet of Things (IoT) accountable to individuals. Accountability is a key to building consumer trust and is mandated by the European Union's general data protection regulation (GDPR). We focus here on the 'external' data subject accountability requirement specified by GDPR and how meeting this requirement turns on surfacing the invisible actions and interactions of connected devices and the social arrangements in which they are embedded. The IoT Databox model is proposed as an in principle means of enabling accountability and providing individuals with the mechanisms needed to build trust into the IoT.

Notions like 'Big Data' and the 'Internet of Things' turn upon anticipated harvesting of personal data through ubiquitous computing and networked sensing systems. It is largely presumed that understandings of people's everyday interactions will be relatively easy to 'read off' of such data and that this, in turn, poses a privacy threat. An ethnographic study of how people account for sensed data to third parties uncovers serious challenges to such ideas. The study reveals that the legibility of sensor data turns upon various orders of situated reasoning involved in articulating the data and making it accountable. Articulation work is indispensable to personal data sharing and raises real requirements for networked sensing systems premised on the harvesting of personal data.

Abstract. We present fieldwork findings from the deployment of an interactive sensing system that supports the work of energy advisors who give face-to-face advice to low-income households in the UK. We focus on how the system and the data it produced are articulated in the interactions between professional energy advisors and their clients, and how they collaboratively anticipate, rehearse, and perform data work. In addition to documenting how the system was appropriated in advisory work, we elaborate the 'overhead cost' of building collaborative action into connected devices and sensing systems, and the commensurate need to support discrete workflows and accountability systems to enable the methodical incorporation of the IoT into collaborative action. We contribute an elaboration of the social, collaborative methods of data work relevant to those who seek to design and study collaborative IoT systems.

We are all increasingly the subjects of data collection and processing systems that use data generated both about and by us to provide and optimise a wide range of services. Means for others to collect and process data that concerns each of us -often referred to possessively as "your data"are only increasing with the long-heralded advent of the Internet of Things just the latest example. As a result, means to enable personal data management is generally recognised as a pressing societal issue.We have previously proposed that one such means might be realised by the Databox, a collection of physical and cloudhosted software components that provide for an individual data subject to manage, log and audit access to their data by other parties. In this paper we elaborate on this proposal, describing the software architecture we are developing, and the current status of a prototype implementation. We conclude with a brief discussion of Databox's limitations.

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The UK government has set ambitious targets to reduce carbon emissions, and lowering energy demand within workplaces is important to help meet these. With the rollout of smart metres and the availability of more fine-grained energy monitoring equipment for the workplace, it is increasingly possible to disaggregate collective energy consumption and apportion this among building users. This article presents an interdisciplinary perspective on the rationale and feasibility of different approaches to apportionment to motivate staff to reduce energy consumption. Our review indicates greatest potential for energy saving when consumption is apportioned to small to medium-sized groups, rather than individuals or entire buildings, particularly when they represent existing communities to which staff members strongly identify. We highlight the complexity of technical, psychological, social and organisational factors that not only inspire, but also often confound, efforts to innovate in this area.

The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. ABSTRACTFuture energy systems that rely on renewable energy may bring about a radical shift in how we use energy in our homes. We developed and prototyped a future scenario with highly variable, real-time electricity prices due to a grid that mainly relies on renewables. We designed and deployed an agent-based interactive system that enables users to effectively operate the washing machine in this scenario. The system is used to book timeslots of washing machine use so that the agent can help to minimize the cost of a wash by charging a battery at times when electricity is cheap. We carried out a deployment in 10 households in order to uncover the socio-technical challenges around integrating new technologies into everyday routines. The findings reveal tensions that arise when deploying a rationalistic system to manage contingently and socially organized domestic practices. We discuss the trade-offs between utility and convenience inherent in smart grid applications; and illustrate how certain design choices position applications along this spectrum.

This paper offers a sociological perspective on data protection regulation and its relevance to design. From this perspective, proposed regulation in Europe and the USA seeks to create a new economic actor-the consumer as personal data trader-through new legal frameworks that shift the locus of agency and control in data processing towards the individual consumer or ''data subject''. The sociological perspective on proposed data regulation recognises the reflexive relationship between law and the social order, and the commensurate needs to balance the demand for compliance with the design of computational tools that enable this new economic actor. We present the Databox model as a means of providing data protection and allowing the individual to exploit personal data to become an active player in the emerging data economy.