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.

This paper presents the results of an exploratory quantitative analysis of gendered contributions to the online mapping project OpenStreetMap (OSM), in which previous research has identified a strong male participation bias. On these grounds, theories of representation in volunteered geographic information (VGI) have argued that this kind of crowdsourced data fails to embody the geospatial interests of the wider community. The observed effects of the bias however, remain conspicuously absent from discourses of VGI and gender, which proceed with little sense of impact. This study addresses this void by analysing OSM contributions by gender and thus identifies differences in men’s and women’s mapping practices. An online survey uniquely captured the OSM IDs as well as the declared gender of 293 OSM users. Statistics relating to users’ editing and tagging behaviours openly accessible via the ‘how did you contribute to OSM’ wiki page were subsequently analysed. The results reveal that volumes of overall activity as well editing and tagging actions in OSM remain significantly dominated by men. They also indicate subtle but impactful differences in men’s and women’s preferences for modifying and creating data, as well as the tagging categories to which they contribute. Discourses of gender and ICT, gender relations in online VGI environments and competing motivational factors are implicated in these observations. As well as updating estimates of the gender participation bias in OSM, this paper aims to inform and stimulate subsequent discourses of gender and representation towards a new rationale for widening participation in VGI.

This study aims to capture the online experiences of young people when interacting with algorithm mediated systems and their impact on their well-being. We draw on qualitative (focus groups) and quantitative (survey) data from a total of 260 young people to bring their opinions to the forefront while eliciting discussions. The results of the study revealed the young people’s positive as well as negative experiences of using online platforms. Benefits such as convenience, entertainment and personalised search results were identified. However, the data also reveals participants’ concerns for their privacy, safety and trust when online, which can have a significant impact on their well-being. We conclude by recommending that online platforms acknowledge and enact on their responsibility to protect the privacy of their young users, recognising the significant developmental milestones that this group experience during these early years, and the impact that algorithm mediated systems may have on them. We argue that governments need to incorporate policies that require technologists and others to embed the safeguarding of users’ well-being within the core of the design of Internet products and services to improve the user experiences and psychological well-being of all, but especially those of children and young people.

The IEEE P7003 Standard for Algorithmic Bias Considerations is one of eleven IEEE ethics related standards currently under development as part of the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. The purpose of the IEEE P7003 standard is to provide individuals or organizations creating algorithmic systems with development framework to avoid unintended, unjustified and inappropriately differential outcomes for users. In this paper, we present the scope and structure of the IEEE P7003 draft standard, and the methodology of the development process.

Children and young people make extensive and varied use of digital and online technologies, yet issues about how their personal data may be collected and used by online platforms are rarely discussed. Additionally, despite calls to increase awareness, schools often do not cover these topics, instead focusing on online safety issues, such as being approached by strangers, cyberbullying or access to inappropriate content. This paper presents the results of one of the activities run as part of eleven workshops with 13-18 year olds, using co-designed activities to encourage critical thinking. Sets of 'data cards' were used to stimulate discussion about sharing and selling of personal data by online technology companies. Results highlight the desire and need for increased awareness about the potential uses of personal data amongst this age group, and the paper makes recommendations for embedding this into school curriculums as well as incorporating it into interaction design, to allow young people to make informed decisions about their online lives.

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.