The theoretical concept of trust has been identified as highly important to the successful design of intelligent technologies such as autonomous vehicles (AVs). In human-centred transport research this has resulted in a focus on trust in the technical design of future AVs and has raised the question of how the conditions that form trust change as technologies become more intelligent. In this article we discuss the first stage of an interdisciplinary project that brought together ethnographic and experimental user studies into trust in intelligent cars. This stage focused on the development of an interdisciplinary methodological framework for the user studies, through a review of 258 empirical HCI research articles on trust in automation and AVs. The review investigated the following research questions: a) what are the key themes in HCI methodologies used to research trust in automation and AVs; b) how do they account for trust in AVs as part of wider contexts; and c) how can these methodologies be developed to include more than momentary and individual human-machine interactions. We found that while theoretical understandings of trust in automated technologies acknowledge the relevance of the wider context in which the interaction occurs, existing methodologies predominantly involve experimental studies in simulated environments with a focus on reliance related aspects of trust. We identified that ethnographic user studies can potentially contribute to new connections between theoretical understandings and conventional experimental methods. Therefore, we propose a framework for an interdisciplinary approach that combines experimental and ethnographic methodologies to investigate trust in AVs. © 2020 The Authors

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.

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.