We present Sociotechnical Challenge Modeling (STCM), a workshop-based design method to help healthcare and social welfare practitioners identify and address sociotechnical challenges in machine learning (ML) deployments. We evaluated STCM in a field experiment with two UK organizations, involving 26 practitioners including managers, data scientists, and frontline care professionals. The evaluation found that STCM cultivated a sociotechnical perspective by revealing interdependencies between ML tools and organizational practices. The physical cards stimulated exchange and experimentation, while the workshop fostered collaboration across disciplines. However, participants found predefined countermeasures too prescriptive, which prompted revisions to support more open-ended ideation. Our contributions are a novel design method for anticipating and mitigating sociotechnical challenges of ML in care settings, and an empirical evaluation of its perceived value and limitations. To support adoption and further research, all STCM materials, including editable card templates and worksheets, are available at: https://bit.ly/4plXkfi.

In recent years, human traffickers have increasingly relied on social media to target and recruit victims. However, limited research has been conducted on the recruitment methods used for human trafficking on social media, particularly in developing countries. Through in-person and digital observations, interviews, and case analysis, our paper sheds light on the tactics used by traffickers on social media to lure people into trafficking and highlights the characteristics of suspicious job advertisements used for human trafficking recruitment. Our paper also discusses the investigative techniques employed by anti-trafficking experts to address recruitment and the challenges they face when identifying and investigating fraudulent jobs. Based on these findings, we provide recommendations on how technology can help tackle the problem of recruiting people online, and suggest design implications to improve the safety of social media platforms. Furthermore, we discuss the challenges and ethical considerations in developing anti-trafficking software and provide suggestions for addressing these challenges.

Politicians and care associations advocate for the use of machine learning (ML) systems to improve the delivery of adult social services. Yet, guidance on how to implement ML systems remains limited and research indicates that future implementation efforts are likely to encounter difficulties. We aim to enhance the understanding of ML system implementations by conducting a longitudinal field study with a team responsible for deploying a ML system within an adult social services department. The ML system implementation represented a cross-organisational effort to facilitate the department’s transition to a proactive practice. Throughout this process, stakeholders adapted to numerous challenges in real-time. This study makes three contributions. First, we provide a description of how ML systems are implemented and highlight practical challenges. Second, we illustrate the utility of HCI knowledge in designing workflows for ML-assisted preventative care programmes. Finally, we provide recommendations for future deployments of ML systems in social care.

Remote driving is a promising strategy for helping Autonomous Vehicles (AVs) navigate many environments where edge cases may otherwise limit their abilities. For some companies, remote driving is an alternative to AVs altogether. Much remote driving research has taken place in simulated or controlled environments with novice operators, leaving the needs of operators with real-world experience under-explored. This research aims to understand if experienced operators are satisfied with current production remote driving systems, if they adapt to the difference in control, and how their job satisfaction compares to in-vehicle safety driving. This paper briefly overviews recent remote driving research and presents results from a questionnaire and a semi-structured interview with experienced teleoperators. The findings indicate that operators do adjust to the new domain, but latency and network reliability remain a challenge. Likewise, standardised training practices for operators are found to be lacking.

Physically embodied artificial agents, or robots, are being incorporated into various practical and social contexts, from self-driving cars for personal transportation to assistive robotics in social care. To enable these systems to better perform under changing conditions, designers have proposed to endow robots with varying degrees of autonomous capabilities and the capacity to move between them—an approach known as variable autonomy. Researchers are beginning to understand how robots with fixed autonomous capabilities influence a person’s sense of autonomy, social relations, and, as a result, notions of responsibility; however, addressing these topics in scenarios where robot autonomy dynamically changes is underexplored. To establish a research agenda for variable autonomy that emphasises the responsible design and use of robotics, we conduct a developmental review. Based on a sample of 42 papers, we provide a synthesised definition of variable autonomy to connect currently disjointed research efforts, detail research approaches in variable autonomy to strengthen the empirical basis for subsequent work, characterise the dimensions of variable autonomy, and present design guidelines for variable autonomy research based on responsible robotics.

Widespread adoption of artificial intelligence (AI) technologies is substantially affecting the human condition in ways that are not yet well understood. Negative unintended consequences abound including the perpetuation and exacerbation of societal inequalities and divisions via algorithmic decision making. We present six grand challenges for the scientific community to create AI technologies that are human-centered, that is, ethical, fair, and enhance the human condition. These grand challenges are the result of an international collaboration across academia, industry and government and represent the consensus views of a group of 26 experts in the field of human-centered artificial intelligence (HCAI). In essence, these challenges advocate for a human-centered approach to AI that (1) is centered in human well-being, (2) is designed responsibly, (3) respects privacy, (4) follows human-centered design principles, (5) is subject to appropriate governance and oversight, and (6) interacts with individuals while respecting human’s cognitive capacities. We hope that these challenges and their associated research directions serve as a call for action to conduct research and development in AI that serves as a force multiplier towards more fair, equitable and sustainable societies.

In commentary driving, drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a better understanding and awareness of their surroundings. In the intelligent vehicle context, automated driving commentary can provide intelligible explanations about driving actions, thereby assisting a driver or an end-user during driving operations in challenging and safety-critical scenarios. In this paper, we conducted a field study in which we deployed a research vehicle in an urban environment to obtain data. While collecting sensor data of the vehicle’s surroundings, we obtained driving commentary from a driving instructor using the think-aloud protocol. We analysed the driving commentary and uncovered an explanation style; the driver first announces his observations, announces his plans, and then makes general remarks. He also makes counterfactual comments. We successfully demonstrated how factual and counterfactual natural language explanations that follow this style could be automatically generated using a transparent tree-based approach. Generated explanations for longitudinal actions (e.g., stop and move) were deemed more intelligible and plausible by human judges compared to lateral actions, such as lane changes. We discussed how our approach can be built on in the future to realise more robust and effective explainability for driver assistance as well as partial and conditional automation of driving functions.

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environment and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs’ behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have ‘seen’, done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing work in explainable autonomous driving. First, we open by providing a motivation for explanations by highlighting the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their AV explanation requirements. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, vehicle control, and system management). Finally, we discuss pertinent challenges and provide recommendations including a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explanation provisions in autonomous driving.

The safe deployment of autonomous vehicles (AV s) in real world scenarios requires that AV s are accountable. One way of ensuring accountability is through the provision of explanations for what the vehicles have ‘seen’, done and might do in a given scenario. Intelligible explanations can help developers and regulators to assess AV s' behaviour, and in turn, uphold accountability. In this paper, we propose an interpretable (tree-based) and user-centric approach for explaining autonomous driving behaviours. In a user study, we examined different explanation types instigated by investigatory queries. We conducted an experiment to identify scenarios that require explanations and the corresponding appropriate explanation types for such scenarios. Our findings show that an explanation type matters mostly in emergency and collision driving conditions. Also, providing intelligible explanations (especially contrastive types) with causal attributions can improve accountability in autonomous driving. The proposed interpretable approach can help realise such intelligible explanations with causal attributions.

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.

Beyond being the world's largest social network, Facebook is for many also one of its greatest sources of digital distraction. For students, problematic use has been associated with negative effects on academic achievement and general wellbeing. To understand what strategies could help users regain control, we investigated how simple interventions to the Facebook UI affect behaviour and perceived control. We assigned 58 university students to one of three interventions: goal reminders, removed newsfeed, or white background (control). We logged use for 6 weeks, applied interventions in the middle weeks, and administered fortnightly surveys. Both goal reminders and removed newsfeed helped participants stay on task and avoid distraction. However, goal reminders were often annoying, and removing the newsfeed made some fear missing out on information. Our findings point to future interventions such as controls for adjusting types and amount of available information, and flexible blocking which matches individual definitions of 'distraction'.

Social media platforms provide an increasingly popular means for individuals to share content online. Whilst this produces undoubted societal benefits, the ability for content to be spontaneously posted and reposted creates an ideal environment for rumour and false/malicious information to spread rapidly. When this occurs it can cause significant harm and can be characterised as a “digital wildfire.” In this article, we demonstrate that the propagation and regulation of digital wildfires form important topics for research and conduct an overview of existing work in this area. We outline the relevance of a range of work from the computational and social sciences, including a series of insights into the propagation of rumour and false/malicious information. We argue that significant research gaps remain—for instance, there is an absence of systematic studies on the effects of digital wildfires and there is a need to combine empirical research with a consideration of how the responsible governance of social media can be determined. We propose an agenda for research that establishes a methodology to explore in full the propagation and regulation of unverified content on social media. This agenda promotes high-quality interdisciplinary research that will also inform policy debates.

We explore the ethical implications of HCI's turn to the ‘cultural’. This is motivated by an awareness of how cultural applications, in our case interactive performances, raise ethical issues that may challenge established research ethics processes. We review research ethics, HCI's engagement with ethics and the ethics of theatrical performance. Following an approach grounded in Responsible Research Innovation, we present the findings from a workshop in which artists, curators, commissioners, and researchers explored ethical challenges revealed by four case studies. We identify six ethical challenges for HCI's engagement with cultural applications: transgression, boundaries, consent, withdrawal, data, and integrity. We discuss two broader implications of these: managing tensions between multiple overlapping ethical frames; and the importance of managing ethical challenges during and after an experience as well as beforehand. Finally, we discuss how our findings extend previous discussions of Value Sensitive Design in HCI.

Given the potent and pervasive nature of modern technologies, this paper lays out the complexities involved in achieving responsible design. In order to do this we will first compare an emerging policy-oriented programme of research known as RRI (Responsible Research and Innovation) with initiatives in HCI. A focus on the similarities and differences may highlight to what extent responsibility is already and successfully embedded within the concerns and practices of design and use, and what may yet need to be incorporated for responsible design. The paper then discusses the challenges of 'naturalising' the very ambitious programme of RRI within specific design activities and concerns, through the lens of four analytic concepts: reflexivity; responsiveness; inclusion; and anticipation. Finally, we make a case for a pragmatic, 'unromantic', but engaged reinterpretation of RRI for HCI.

The discourse concerning computer ethics qualifies as a reference discourse for ethics-related IS research. Theories, topics and approaches of computer ethics are reflected in IS. The paper argues that there is currently a broader development in the area of research governance, which is referred to as ‘responsible research and innovation’ (RRI). RRI applied to information and communication technology (ICT) addresses some of the limitations of computer ethics and points toward a broader approach to the governance of science, technology and innovation. Taking this development into account will help IS increase its relevance and make optimal use of its established strengths.

The potential social consequences that may arise as a result of the development and widespread use of ICT are of increasing interest to the general public, policymakers and researchers. Prominent examples include transformations of our concept of privacy when using social networking and other websites, ownership and control of personal data, and the ways crowd-sourced information transform how events are coordinated and how they unfold in real-time. Although there is broad acceptance that questions of professional responsibility are relevant to ICT it is often unclear how this could be achieved or how responsibilities should be defined and managed when considering the potential social consequences of ICT. `Responsible Research and Innovation' (RRI) has emerged in Europe proposing approaches for researchers to identify and consider the potential social consequences and impact of their research outputs within the entire research and innovation lifecycle. First, it asks researchers to take on a practice of critical reflection considering the potential societal impacts of their research outputs, and second to include the general public in a dialogue around the development of research goals and strategy. We discuss findings from interviews conducted with a broad range of stakeholders regarding challenges to identifying, debating and resolving social and ethical concerns associated with ICT research and practice. We also consider how we might extend existing or develop new approaches that facilitate both critical reflection and wider participation within the entire research and innovation lifecycle.

A concern with “embodied action” has informed both the analysis of everyday action through technologies and also suggested ways of designing innovative systems. In this article, we consider how these two programs, the analysis of everyday embodied interaction on the one hand, and the analysis of technically-mediated embodied interaction on the other, are interlinked. We draw on studies of everyday interaction to reveal how embodied conduct is embedded in the environment. We then consider a collaborative technology that attempts to provide a coherent way of presenting life-sized embodiments of participants alongside particular features of the environment. These analyses suggest that conceptions of embodied action should take account of the interactional accomplishment of activities and how these are embedded in the material environment.

There are numerous settings where people examine, scrutinize and discuss the details of images in the course of their work. In most medical domains, scans and x-rays are used in the diagnosis of cases; in most areas of science, methods of visualization have been adopted to assist in the analysis of data; and images of different kinds are critical for many research fields in the social sciences and humanities. It is not surprising that recently technologies have been proposed to assist with the analysis and examination of images. In this paper, we consider requirements for technologies in a rather distinctive domain of research, the classics. Drawing upon an analysis of the detailed ways in which classicists work with digital images, we discuss the requirements for systems to support researchers in this domain, and also provide further considerations on the general development of image processing technologies and visualization techniques.

Recent technologies to support collaborative work have sought, in various ways, to enhance an individual's awareness of another's activities. Through a range of diverse technologies developers have endeavoured to provide users with capabilities that allow them to monitor, either passively or actively, what others are doing. In this paper we aim to examine awareness by analysing a setting where one of the responsibilities of the staff is to oversee, through a set of technologies, a complex environment in order to monitor the various spaces and locations in the local domain, the individuals who move through these spaces, and the events that occur in it. We outline the resources they utilise to make sense of what personnel see on the screens and to initiate collaborative action with colleagues. We conclude by discussing how such analyses can inform the design of novel systems which aim to support awareness and monitoring of environments. More critically we draw on this study to reconsider the conception of awareness utilised within Computer Supported Cooperative Work and other fields where technological solutions are being proposed to support individuals to monitor, whether peripherally or not, locations, activities and other individuals in digital environments.

Methods for requirements elicitation have emphasized techniques for their elicitation and representation. The conception of tasks embodied in these methods is often vague or left implicit and generally characterized in individualistic terms. The authors draw from empirical materials to reveal the social and collaborative nature of task that is also overlooked in participative design or in attempts to elicit multiple viewpoints of an activity. Exploring the socio-interactional nature of activities leads to some radical implications for the technological design. An approach that utilizes ethnographic studies of real-world settings with detailed analysis of interactions of the participants may make an important contribution to the development of requirements method.>

A planner which generates advice about the procedures which should be carried out by a human agent in order to achieve a goal is described. The fact that the agent is a person, not a robot, makes it possible to develop plans cooperatively with the user in the course of a dialogue, but imposes special requirements on the planner. The planner should be capable of taking advantage of the user's knowledge and abilities; of providing partial plans; of planning even in the absence of complete knowledge about the user's current state; of re-planning when the execution does not succeed or the situation changes; and of providing explanations of its advice. The paper considers the implications of these requirements on the design of such an advisory planner, implemented as part of the ‘Advice System’, a knowledge-based system for advising members of the public about welfare benefits.