arXiv subjects

Andy Crabtree

Publications and source records attributed to Andy Crabtree.

14 recordsLinked to original sources

arXiv2026

Author response to commentaries on H is for Human and How (Not) to Evaluate Qualitative Research in HCI

This is the authors response to commentaries on the original article H is for Human and How (Not) to Evaluate Qualitative Research in HCI, https://doi.org/10.1080/07370024.2025.2475743 Commentaries were provided by: Jeffrey Bardzell, https://doi.org/10.1080/07370024.2025.2612474 Alan Blackwell, https://doi.org/10.1080/07370024.2025.2591878 Paul Dourish, https://doi.org/10.1080/07370024.2025.2594529 Bonnie Nardi, https://doi.org/10.1080/07370024.2025.2596752 Peter Pirolli, https://doi.org/10.1080/07370024.2025.2596745 Jennifer Rode, https://doi.org/10.1080/07370024.2025.2598800 Peter Tolmie, https://doi.org/10.1080/07370024.2025.2591872 Please feel free to copy, redistribute, adapt, and build on any part of this article in accordance with the CC BY 4.0 license: https://creativecommons.org/licenses/by/4.0/

cs.HC↗

arXiv2026

How to Analyse Interviews: A Documentary Method of Interpretation

Interviews are commonplace in HCI. This paper presents a documentary method of interpretation (DMI) for the analysis of the topical organisation of talk within a corpus of transcripts, topics that are endogenous to it and elaborate patterns of social reasoning about issues of relevance to research. The DMI reflexively enables endogenous topic analysis (ETA). We contrast ETA with established qualitative approaches, including qualitative content analysis, grounded theory, interpretative phenomenological analysis, and thematic analysis, to draw out its distinctive character and unique contribution. Unlike established methods, ETA does not require that the analyst be proficient in qualitative analysis, or have knowledge of underlying theories and methods in the social sciences. ETA relies on the DMI, which is a members method, not a social science method, and mastery of natural language; a competence most people already possess.

cs.HC↗

arXiv2024

H is for Human and How (Not) To Evaluate Qualitative Research in HCI

Concern has recently been expressed by HCI researchers as to the inappropriate treatment of qualitative studies through a positivistic mode of evaluation that places emphasis on metrics and measurement. This contrasts with the nature of qualitative research, which privileges interpretation and understanding over quantification. This paper explains the difference between positivism and interpretivism, the limits of quantification in human science, the distinctive contribution of qualitative research, and how quality assurance might be provided for in the absence of numbers via five basic criteria that reviewers may use to evaluate qualitative studies on their own terms.

cs.HC↗

arXiv2024

Responsibility and Regulation: Exploring Social Measures of Trust in Medical AI

This paper explores expert accounts of autonomous systems (AS) development in the medical device domain (MD) involving applications of artificial intelligence (AI), machine learning (ML), and other algorithmic and mathematical modelling techniques. We frame our observations with respect to notions of responsible innovation (RI) and the emerging problem of how to do RI in practice. In contribution to the ongoing discourse surrounding trustworthy autonomous system (TAS) [29], we illuminate practical challenges inherent in deploying novel AS within existing governance structures, including domain specific regulations and policies, and rigorous testing and development processes, and discuss the implications of these for the distribution of responsibility in novel AI deployment.

cs.HC↗

arXiv2024

AI and the Iterable Epistopics of Risk

Abstract. The risks AI presents to society are broadly understood to be manageable through general calculus, i.e., general frameworks designed to enable those involved in the development of AI to apprehend and manage risk, such as AI impact assessments, ethical frameworks, emerging international standards, and regulations. This paper elaborates how risk is apprehended and managed by a regulator, developer and cyber-security expert. It reveals that risk and risk management is dependent on mundane situated practices not encapsulated in general calculus. Situated practice surfaces iterable epistopics, revealing how those involved in the development of AI know and subsequently respond to risk and uncover major challenges in their work. The ongoing discovery and elaboration of epistopics of risk in AI a) furnishes a potential program of interdisciplinary inquiry, b) provides AI developers with a means of apprehending risk, and c) informs the ongoing evolution of general calculus.

cs.CY↗

arXiv2024

Experiencing the Future Mundane: Configuring Design Fiction as Breaching Experiment

This paper introduces a novel methodological approach for surfacing the acceptability and adoption challenges that confront future and emerging technologies from the perspective of mundane action, in which they will ultimately be embedded and used. This novel approach configures design fiction as a breaching experiment to surface taken for granted background expectancies that are fateful for acceptability and adoption. We explain the logic of this new interdisciplinary method and present a concrete case to demonstrate its viability: a design fiction called Experiencing the Future Mundane (EFM), which depicts a future world in which watching TV is driven by smart adaptive media. We explicate the design of the EFM, how it was configured to breach common sense knowledge and surface taken for granted background expectancies concerning how watching TV works and is expected to work, the acceptability and adoption challenges that emerge from user engagement with the experience, and how this novel approach may be adopted more broadly.

cs.HC↗

arXiv2022

Legal Provocations for HCI in the Design and Development of Trustworthy Autonomous Systems

We consider a series of legal provocations emerging from the proposed European Union AI Act 2021 (AIA) and how they open up new possibilities for HCI in the design and development of trustworthy autonomous systems. The AIA continues the by design trend seen in recent EU regulation of emerging technologies. The AIA targets AI developments that pose risks to society and citizens fundamental rights, introducing mandatory design and development requirements for high-risk AI systems (HRAIS). These requirements regulate different stages of the AI development cycle including ensuring data quality and governance strategies, mandating testing of systems, ensuring appropriate risk management, designing for human oversight, and creating technical documentation. These requirements open up new opportunities for HCI that reach beyond established concerns with the ethics and explainability of AI and situate AI development in human-centered processes and methods of design to enable compliance with regulation and foster societal trust in AI.

cs.HC↗

arXiv2020

Visions, Values, and Videos: Revisiting Envisionings in Service of UbiComp Design for the Home

UbiComp has been envisioned to bring about a future dominated by calm computing technologies making our everyday lives ever more convenient. Yet the same vision has also attracted criticism for encouraging a solitary and passive lifestyle. The aim of this paper is to explore and elaborate these tensions further by examining the human values surrounding future domestic UbiComp solutions. Drawing on envisioning and contravisioning, we probe members of the public (N=28) through the presentation and focus group discussion of two contrasting animated video scenarios, where one is inspired by "calm" and the other by "engaging" visions of future UbiComp technology. By analysing the reasoning of our participants, we identify and elaborate a number of relevant values involved in balancing the two perspectives. In conclusion, we articulate practically applicable takeaways in the form of a set of key design questions and challenges.

cs.HC↗

arXiv2019

Breaching the Future: Understanding Human Challenges of Autonomous Systems for the Home

The domestic environment is a key area for the design and deployment of autonomous systems. Yet research indicates their adoption is already being hampered by a variety of critical issues including trust, privacy and security. This paper explores how potential users relate to the concept of autonomous systems in the home and elaborates further points of friction. It makes two contributions. One methodological, focusing on the use of provocative utopian and dystopian scenarios of future autonomous systems in the home. These are used to drive an innovative workshop-based approach to breaching experiments, which surfaces the usually tacit and unspoken background expectancies implicated in the organisation of everyday life that have a powerful impact on the acceptability of future and emerging technologies. The other contribution is substantive, produced through participants efforts to repair the incongruity or "reality disjuncture" created by utopian and dystopian visions, and highlights the need to build social as well as computational accountability into autonomous systems, and to enable coordination and control.

cs.HC↗

arXiv2019

Zest: REST over ZeroMQ

In this paper, we introduce Zest (REST over ZeroMQ), a middleware technology in support of an Internet of Things (IoT). Our work is influenced by the Constrained Application Protocol (CoAP) but emphasises systems that can support fine-grained access control to both resources and audit information, and can provide features such as asynchronous communication patterns between nodes. We achieve this by using a hybrid approach that combines a RESTful architecture with a variant of a publisher/subscriber topology that has enhanced routing support. The primary motivation for Zest is to provide inter-component communications in the Databox, but it is applicable in other contexts where tight control needs to be maintained over permitted communication patterns.

cs.DC↗

arXiv2018

Enabling Trusted App Development @ The Edge

We present the Databox application development environment or SDK as a means of enabling trusted IoT app development at the network edge. The Databox platform is a dedicated domestic platform that stores IoT, mobile and cloud data and executes local data processing by third party apps to provide end-user control over data flow and enable data minimisation. Key challenges for building apps in edge environments concern i. the complexity of IoT devices and user requirements, and ii. supporting privacy preserving features that meet new data protection regulations. We show how the Databox SDK can ease the burden of regulatory compliance and be used to sensitize developers to privacy related issues in the very course of building apps. We present feedback on the SDK's exposure to over 3000 people across a range of developer and industry events.

cs.SE↗

arXiv2018

An Analysis of Home IoT Network Traffic and Behaviour

Internet-connected devices are increasingly present in our homes, and privacy breaches, data thefts, and security threats are becoming commonplace. In order to avoid these, we must first understand the behaviour of these devices. In this work, we analyse network traces from a testbed of common IoT devices, and describe general methods for fingerprinting their behavior. We then use the information and insights derived from this data to assess where privacy and security risks manifest themselves, as well as how device behavior affects bandwidth. We demonstrate simple measures that circumvent attempts at securing devices and protecting privacy.

cs.NI↗

arXiv2018

Demonstrably Doing Accountability in the Internet of Things

This paper explores the importance of accountability to data protection, and how it can be built into the Internet of Things (IoT). The need to build accountability into the IoT is motivated by the opaque nature of distributed data flows, inadequate consent mechanisms, and lack of interfaces enabling end-user control over the behaviours of internet-enabled devices. The lack of accountability precludes meaningful engagement by end-users with their personal data and poses a key challenge to creating user trust in the IoT and the reciprocal development of the digital economy. The EU General Data Protection Regulation 2016 (GDPR) seeks to remedy this particular problem by mandating that a rapidly developing technological ecosystem be made accountable. In doing so it foregrounds new responsibilities for data controllers, including data protection by design and default, and new data subject rights such as the right to data portability. While GDPR is technologically neutral, it is nevertheless anticipated that realising the vision will turn upon effective technological development. Accordingly, this paper examines the notion of accountability, how it has been translated into systems design recommendations for the IoT, and how the IoT Databox puts key data protection principles into practice.

cs.HC↗

arXiv2016

Valorising the IoT Databox: Creating Value for Everyone

The Internet of Things (IoT) is expected to generate large amounts of heterogeneous data from diverse sources including physical sensors, user devices, and social media platforms. Over the last few years, significant attention has been focused on personal data, particularly data generated by smart wearable and smart home devices. Making personal data available for access and trade is expected to become a part of the data driven digital economy. In this position paper, we review the research challenges in building personal Databoxes that hold personal data and enable data access by other parties, and potentially thus sharing of data with other parties. These Databoxes are expected to become a core part of future data marketplaces.

cs.CY↗