Exploring Participatory Design Methods to Engage with Arab Communities

ArabHCI is an initiative inaugurated in CHI17 SIG Meeting that brought together 45+ HCI Arab and non-Arab researchers/practitioners who are conducting/interested in HCI within Arab communities. The goal of this workshop is to start dialogs that leverage our "insider" understanding of HCI research in the Arab context and assert our culture identity in design in order to explore challenges and opportunities for future research. In this workshop, we focus on one of the themes that derived our community discussions in most of the held events. We explore the extent to which participatory approaches in the Arab context are culturally and methodologically challenged. Our goal is to bring researchers/practitioners with success and failure stories while designing with Arab communities to discuss methods, share experiences and learned lessons. We plan to share the results of our discussions and research agenda with the wider CHI community through different social and scholarly channels

An Augmented Reality Game using Face Recognition Technology

In this paper, we explore the coupling of mobile facial recognition technology with the exploitation of non-players as a powerful mechanic in locative augmented reality games. A prototype game is presented which asks players to "capture" the likeness of members of the public. Driven by free-to-play models, and inspired by the phenomenal success of Pokémon GO, we have created an experience where players hunt for and "capture" real creatures in a real world

“No powers, man!”: A student perspective on designing university smart building interactions

Smart buildings offer an opportunity for better performance and enhanced experience by contextualising services and interactions to the needs and practices of occupants. Yet, this vision is limited by established approaches to building management, delivered top-down through professional facilities management teams, opening up an interaction-gap between occupants and the spaces they inhabit. To address the challenge of how smart buildings might be more inclusively managed, we present the results of a qualitative study with student occupants of a smart building, with design workshops including building walks and speculative futuring. We develop new understandings of how student occupants conceptualise and evaluate spaces as they experience them, and of how building management practices might evolve with new sociotechnical systems that better leverage occupant agency. Our findings point to important directions for HCI research in this nascent area, including the need for HBI (Human-Building Interaction) design to challenge entrenched roles in building management

SenseMyStreet: Sensor Commissioning Toolkit for Communities

The rise of big data and smart sensing, with the promise of more educated and informed decisions, has fuelled a shift towards more data-driven decision-making in local and national government. However, we are observing a disconnect between the people who are affected by these decisions and their access to tools and resources to collect data in order to provide the needed evidence for change. To truly democratise this process and for citizens to become active prosumers of data, new mechanisms of citizen data production are needed. In this paper we report on a two-year ethnographic and iterative co-design process with the local community. This work encompassed the design, development and deployment of SenseMyStreet (SeMS), a bespoke sensor commissioning toolkit that enables citizens and community groups to use and commission a city's scientific-grade environmental monitors, determining where they will be located on their streets and collecting data to evidence hyper-local issues. Unlike prior research, which creates alternative data sources to contest city data, our toolkit helps integrate citizen commissioned data into the city datasets used by citizens and decision-makers. Reflecting on the design process and evaluating the ways people engaged with the digital tools of the toolkit, we highlight how commissioning can be configured to promote equity in the smart city, empower citizens to take ownership of issues and facilitate the creation of community networks that utilise the data for local benefit

From Creating Spaces for Civic Discourse to Creating Resources for Action

In this paper, we investigate the role of technology to address the concerns of a civil society group carrying out community-level consultation on the allocation of £1 million of community funds. We explore issues of devolved decision-making through the evaluation of a sociodigital system designed to foster deliberative virtues. We describe the ways in which this group used our system in their consultation practices. Our findings highlight how they adopted our technology to privilege specific forms of expression, ascertain issues in their community, make use of and make sense of community data, and create resources for action within their existing practices. Based on related fieldwork we discuss the impacts of structuring and configuring tools for ‘talk-based’ consultation in order to turn attention to the potential pitfalls and prospects for designing civic technologies that create resources for action for civil society

Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research

2023 Hogg, Al-Zubaidy, Keane, Hughes, Beyer and Maniatopoulos.Introduction: Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. Methods: Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. Results: Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). Conclusion: TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science\u27s translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the “AI chasm” continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice

Evaluating the translation of implementation science to clinical artificial intelligence: a bibliometric study of qualitative research

INTRODUCTION: Whilst a theoretical basis for implementation research is seen as advantageous, there is little clarity over if and how the application of theories, models or frameworks (TMF) impact implementation outcomes. Clinical artificial intelligence (AI) continues to receive multi-stakeholder interest and investment, yet a significant implementation gap remains. This bibliometric study aims to measure and characterize TMF application in qualitative clinical AI research to identify opportunities to improve research practice and its impact on clinical AI implementation. METHODS: Qualitative research of stakeholder perspectives on clinical AI published between January 2014 and October 2022 was systematically identified. Eligible studies were characterized by their publication type, clinical and geographical context, type of clinical AI studied, data collection method, participants and application of any TMF. Each TMF applied by eligible studies, its justification and mode of application was characterized. RESULTS: Of 202 eligible studies, 70 (34.7%) applied a TMF. There was an 8-fold increase in the number of publications between 2014 and 2022 but no significant increase in the proportion applying TMFs. Of the 50 TMFs applied, 40 (80%) were only applied once, with the Technology Acceptance Model applied most frequently (n = 9). Seven TMFs were novel contributions embedded within an eligible study. A minority of studies justified TMF application (n = 51,58.6%) and it was uncommon to discuss an alternative TMF or the limitations of the one selected (n = 11,12.6%). The most common way in which a TMF was applied in eligible studies was data analysis (n = 44,50.6%). Implementation guidelines or tools were explicitly referenced by 2 reports (1.0%). CONCLUSION: TMFs have not been commonly applied in qualitative research of clinical AI. When TMFs have been applied there has been (i) little consensus on TMF selection (ii) limited description of selection rationale and (iii) lack of clarity over how TMFs inform research. We consider this to represent an opportunity to improve implementation science's translation to clinical AI research and clinical AI into practice by promoting the rigor and frequency of TMF application. We recommend that the finite resources of the implementation science community are diverted toward increasing accessibility and engagement with theory informed practices. The considered application of theories, models and frameworks (TMF) are thought to contribute to the impact of implementation science on the translation of innovations into real-world care. The frequency and nature of TMF use are yet to be described within digital health innovations, including the prominent field of clinical AI. A well-known implementation gap, coined as the "AI chasm" continues to limit the impact of clinical AI on real-world care. From this bibliometric study of the frequency and quality of TMF use within qualitative clinical AI research, we found that TMFs are usually not applied, their selection is highly varied between studies and there is not often a convincing rationale for their selection. Promoting the rigor and frequency of TMF use appears to present an opportunity to improve the translation of clinical AI into practice