Autonomous vehicle (AV) algorithms tend to be designed with a techno-solutionism mindset, causing algorithms to fail in real-world applications. This can be attributed to algorithm developers’ lack of routines and knowledge to consider the environments and circumstances AVs are intended to partake in. This paper argues for shifting towards a more responsible human-centred algorithm design (HCAD). It addresses this by demonstrating the different reflective practice qualities obtained by engaging algorithm designers from four companies with ethnographic materials. The study shows that Design Ethnographic (DE) approach allowed developers to consider the value of AVs from sociotechnical perspectives and facilitated collaborative learning and debating about what problems truly need solving to bring societal value. This demonstrates how ethnographically infused HCAD helps expand algorithm developers’ opportunities to participate responsibly in value co-creation for society.

This paper investigates elements of trust in autonomous vehicles (AVs). We contex- tualised autonomous vehicles as part of a socio-technical system to extend previous understandings of trust and explore trust in autonomous vehicles in concrete social contexts. We conducted online co-creation workshops with 22 participants, using design probes to explore trust and AVs in relation to people’s everyday lives. Us- ing a systemic perspective, we show how trust and acceptance depend not only on the underlying AV technology but also - if not more so - on human-to-human rela- tionships and real-life social circumstances. We argue that when investigating issues of trust and automation, the scope of analysis needs to be broadened to include a more complex socio-technical set of (human and non-human) agents, to extend from momentary human-computer interactions to a wider timescale, and be situated in concrete spaces, social networks, and situations. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

It is hard to predict the impact of technology on society before it is developed enough. For example, the issue can be attributed to the need for more cross-sectoral collaboration in the design process. However, a solution for anticipating such outcomes has been proposed through the quadruple helix innovation model, which states that the involvement of government, academia, industry, and the public is essential in innovation systems. The question of how this collaboration can successfully be staged to foresee possible impacts is an empirical endeavour. This paper presents an iterative case study of how ethnographic material can be used to ongoingly tailor speculative co-creation to facilitate responsible innovation (RI) principles. The result is reflected through two lenses; the tools developed in the project to facilitate co-creation activities and the stakeholder reflections evoked through these tools.

It is hard to predict the impact of technology on society before it is developed enough. For example, the issue can be attributed to the need for more cross-sectoral collaboration in the design process. However, a solution for anticipating such outcomes has been proposed through the quadruple helix innovation model, which states that the involvement of government, academia, industry, and the public is essential in innovation systems. The question of how this collaboration can successfully be staged to foresee possible impacts is an empirical endeavour. This paper presents an iterative case study of how ethnographic material can be used to ongoingly tailor speculative co-creation to facilitate responsible innovation (RI) principles. The result is reflected through two lenses; the tools developed in the project to facilitate co-creation activities and the stakeholder reflections evoked through these tools. © 2023 29th Annual Americas Conference on Information Systems, AMCIS 2023. All rights reserved.

This thesis addresses the unresolved issues of responsibility and accountability in autonomous vehicle (AV) development, advocating for human-centred approaches to enhance trustworthiness. While AVs hold the potential for improved safety, mobility, and environmental impact, poorly designed algorithms pose risks, leading to public distrust. Trust research focuses on technology-related aspects but overlooks trust within broader social and cultural contexts. Efforts are underway to understand algorithm design practices, acknowledging their potential unintended consequences. For example, Baumer (2017) advocates human-centred algorithm design (HCAD) to align with user perspectives and reduce risks. HCAD incorporates theoretical, participatory, and speculative approaches, emphasising user and stakeholder engagement. This aligns with broader calls for prioritising societal considerations in technology development (Stilgoe, 2013). The research in this thesis responds to these calls by integrating theories on trust and trustworthiness, autonomous vehicle development, and human-centred approaches in empirical investigations guided by the following research question: “How can human-centred approaches support the development of trustworthy intelligent vehicle technology?” This thesis approaches the question through design ethnography to ground the explorations in people’s real-life routines, practices and anticipations and demonstrate how design ethnographic techniques can infuse AV development with human-centred understandings of people’s trust in AVs. The studies reported in this thesis include a) interviews and participatory observations of algorithm designers, b) interviews and probing with residents, and c) staging collaborative, reflective practice through the design ethnographic materials and co-creation with citizens, city, academic and industry stakeholders, including AV algorithm designers. 

Through these empirical explorations, this thesis suggests an answer to the research question by coining a novel and timely framework for intelligent vehicle development: trustworthy algorithm design (TAD). TAD demonstrates trustworthiness as an ongoing process, not just a measurable outcome from human-technology interactions. It calls to consider autonomous vehicle algorithms as construed through a network of stakeholders, practices, and technologies and, therefore, defines trustworthy algorithm design as a continuous collaborative learning and evolvement process of different disciplines and sectors. Furthermore, the TAD framework suggests that for autonomous vehicle algorithm design to be trustworthy, it must be responsive, interventional, intentional and transdisciplinary. 

The TAD framework integrates ideas and strategies from different well-known trajectories of research in the field of responsible and human-centred technology development: Human-Centred Algorithm Design (Baumer, 2017), algorithms as culture (Seaver, 2017) and Responsible Innovation (Stilgoe et al., 2013). The thesis contributes to this field by empirically investigating how this integrated framework helps expand existing understandings of interactional trust in intelligent technologies and include the relevance of participatory processes of trustworthiness and how these processes are nurtured through cross-sector co-learning and design ethnographic materials.

Emerging technologies—such as autonomous driving (AD) cars, blockchain, robotics, and drones—are increasingly part of popular narratives and industry and policy agendas. They are commonly understood as new digital, data-driven, intelligent, or automated technological innovations in development, or at the cusp of being launched into a market. Thus, the anthropological question of how they might become part of everyday, experiential, possible worlds demands our attention. In this chapter we outline an approach to emerging technologies that is rooted in design anthropology and takes an interventional stance. In doing so we situated design anthropology of emerging technologies within an interdisciplinary field which has tended to be dominated by technologically determinist approaches. Through the example of the notion of trust in AD cars, we show how policy, industry, engineering, and social science approaches configure to provide different and critical understandings. Drawing on our own design ethnographic research, we show how design anthropological attention to people offers an alternative and viable mode of understanding how emerging technologies become part of emerging worlds. © 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

In this chapter, we demonstrate how a design ethnographic approach to future algorithm-powered mobility solutions opens up possibilities to research social implications of automated decision making (ADM) from a situational perspective, by investigating the context of ADM rather than simply observing the technology itself and how it is used. The context of our discussion is one where the development of autonomous vehicles and artificial intelligence (AI) applications, in the service of transportation, has sparked a renewed research interest into shared mobility systems, and how these can respond to emerging challenges of rising traffic congestion and pollution levels. Our research addresses the gap between algorithm-based approaches to designing for optimizing, streamlining, and efficiency, the questions of how these systems and services are activated in people’s everyday life, and how they interfere with decision-making around traveling and shared mobility. We argue that to understand how these services and technologies will be adopted and implemented in society, research attention must be directed to people in real-life situations where this type of ADM operates.

This chapter explains how designing for future intelligent mobility systems is advanced by a human-centered approach, based in design anthropology. It provides an accessible introduction the theory and methodology of this approach, the production of ethnographic insights, and their translation into design probes for use workshops tailored to enable stakeholders to actively co-design future mobility and autonomous vehicle services and outlines the potentials and challenges of engaging diverse stakeholders—from industry and policy to the people who will use future technologies and services—in the development of future mobility. © 2021, Springer Nature Switzerland AG.

In this article we demonstrate how design anthropology theory, methodology and practice can be mobilised to create interventions in how possible human futures with emerging technologies are understood and imagined. Drawing on our research into Human Experience and Expectations of Autonomous Driving (AD) cars we show how: we engaged ethnographic insights to redefine concepts of trust and sharing which contest dominant problem-solution narratives; and we mobilised these insights in applied contexts, through our AD Futures cards which employ ethnographic quotes and examples to disrupt common assumptions, align stakeholders with everyday experience, and pose new questions. © 2020 Elsevier Ltd. All rights reserved.