Background: Digital contact tracing is employed to monitor and manage the spread of COVID-19. However, to be effective the system must be adopted by a substantial proportion of the population. Studies of mostly hypothetical contact tracing apps show generally high acceptance, but little is known about the drivers and barriers to adoption of deployed systems. Objective: The aim of this study was to investigate adoption of and attitudes toward the NHS (National Health Service) COVID-19 smartphone app, the digital contact tracing solution in the United Kingdom. Methods: An online survey based on the extended Technology Acceptance Model with the added factor of trust was carried out with a representative sample of the UK population. Statistical analysis showed adoption rates, attitudes toward and trust in the app, and compliance with self-isolation advice and highlighted differences for vulnerable populations (ie, older adults aged 65 years and over and members of Black, Asian, and minority ethnic [BAME] communities). Results: A total of 1001 participants took part in the study. Around half of the participants who had heard of the NHS COVID-19 mobile phone app (490/963, 50.9%; 95% CI 47.8%-54.0%) had downloaded and kept the app, but more than one-third (345/963, 35.8%; 95% CI 32.8%-38.8%) either did not intend to download it or had deleted it. Significantly more BAME respondents than White respondents had deleted the app (16/115, 13.9%; 95% CI 11.8%-16.0%, vs 65/876, 7.4%; 95% CI 5.8%-9.0%), and significantly more older adults 65 years and over than those under 65 years did not intend to download it (44/127, 34.6%; 95% CI 31.7%-37.5%, vs 220/874, 25.2%; 95% CI 22.5%-27.9%). Broadly, one of the reasons for uptake was to help the NHS and other people, especially among older adults, although significantly fewer BAME participants agreed that they did so to help the NHS. Reported compliance with received notifications to self-isolate was high but was significantly lower than reported intended compliance without received notifications. Only one-fifth (136/699, 19.5%; 95% CI 17.0%-22.0%) of participants understood that the decision to send self-isolation notifications was automated by the app. There were a range of significantly more negative views among BAME participants, including lower trust in the NHS, while older adults were often significantly more positive. Respondents without the app reported significantly lower trust and more negative views toward the app and were less likely to report that they understood how the app works. Conclusions: While compliance on the part of the approximately 50% of participants who had the app was fairly high, there were issues surrounding trust and understanding that hindered adoption and, therefore, the effectiveness of digital contact tracing, particularly among BAME communities. This study highlights that more needs to be done to improve adoption among groups who are more vulnerable to the effects of the virus in order to enhance uptake and acceptance of contact tracing apps.

Researchers often find it hard to know where, when and how to start when applying Responsible Innovation approaches to their own research projects and proposals. Based on experience supporting a range of researchers and projects, we have developed a small set of concept cards, the Responsible Research and Innovation (RRI) Prompts and Practice cards, which highlight 18 different aspects of RRI, each with key questions and prompts for action. Initial use with groups of researchers and PhD students has found them to be accessible and effective in prompting reflection and discussion, including raising previously unconsidered aspects of responsible innovation. Based on this feedback we are now developing a second release of the cards.

Algorithmic systems are increasingly deployed to make decisions that people used to make. Perceptions of these systems can significantly influence their adoption, yet, broadly speaking, users’ understanding of the internal working of these systems is limited. To explore users’ perceptions of algorithmic systems, we developed a prototype e-recruitment system called Algorithm Playground where we offer the users a look behind the scenes of such systems, and provide “how” and “why” explanations on how job applicants are ranked by their algorithms. Using an online study with 110 participants, we measured perceived fairness, transparency and trustworthiness of e-recruitment systems. Our results show that user understanding of the data and reasoning behind candidates’ rankings and selection evoked some positive attitudes as participants rated our platform to be fairer, more reliable, transparent, and trustworthy than the e-recruitment systems they have used in the past.

Purpose: The purpose of this paper is to report on empirical work conducted to open up algorithmic interpretability and transparency. In recent years, significant concerns have arisen regarding the increasing pervasiveness of algorithms and the impact of automated decision-making in our lives. Particularly problematic is the lack of transparency surrounding the development of these algorithmic systems and their use. It is often suggested that to make algorithms more fair, they should be made more transparent, but exactly how this can be achieved remains unclear. Design/methodology/approach: An empirical study was conducted to begin unpacking issues around algorithmic interpretability and transparency. The study involved discussion-based experiments centred around a limited resource allocation scenario which required participants to select their most and least preferred algorithms in a particular context. In addition to collecting quantitative data about preferences, qualitative data captured participants’ expressed reasoning behind their selections. Findings: Even when provided with the same information about the scenario, participants made different algorithm preference selections and rationalised their selections differently. The study results revealed diversity in participant responses but consistency in the emphasis they placed on normative concerns and the importance of context when accounting for their selections. The issues raised by participants as important to their selections resonate closely with values that have come to the fore in current debates over algorithm prevalence. Originality/value: This work developed a novel empirical approach that demonstrates the value in pursuing algorithmic interpretability and transparency while also highlighting the complexities surrounding their accomplishment.