Culturally sensitive design is crucial for developing inclusive technologies, particularly in resource-constrained settings. However, such approaches often oversimplify culture and face challenges in cross-cultural relevance. This study addresses these issues by exploring how participatory design can be both culturally grounded and adaptable across subcultures within African communities. We conducted 13 distributed design workshops with 19 participants, including people with Type 2 Diabetes (T2D), caregivers, and pharmacists, from diverse ethnic groups in Port Harcourt, Nigeria. These workshops informed the design of a mobile health prototype featuring interactive flows in Pidgin English, collaborative care tools, peer support groups, and a calorie prediction feature. The prototype was evaluated by 30 participants through think-aloud sessions and interviews. Findings highlight that while some features aligned with local cultural norms, others were less effective across sociocultural boundaries, even within the same city. We offer insights and methodological guidance for developing digital health tools that are locally relevant and regionally adaptable.

This research study scopes the ethical implications associated with the deployment of social robots within workplace environments, in the context of an increased need of employee wellbeing. Progress in the domains of artificial intelligence (AI) and social robotics present a potential avenue for improving workplace wellbeing. However, it is necessary conduct a comprehensive evaluation of the impacts and ethical considerations associated with these emerging technologies. For this purpose, we carried out an ethical risk assessment for a telepresence robot programmed to function as a social robot in the workplace, which we named ’Cheerbot’. After introducing Cheerbot’s functions, the paper describes an ethical risk assessment process, which involves identifying potential hazards, the likelihood of the hazard occurring, potential consequences (harms), and a risk exposure rating for each hazard. Results are presented for three hypothetical scenarios, and potential mitigations for the highest rated risks are suggested. The findings highlight the value of proactively identifying and mitigating ethical risks from harms, ensuring responsible deployment of robotics aimed at supporting workplace wellbeing.

We present the design of the Memory Machine (MeMa), a technology probe that can store, contextualise, and document media to represent memories. We accumulate vast physical and digital possessions throughout our lives, making it difficult to distinguish value in amassed images, albums, videos, mementos, and music. One option we wanted to explore via MeMa was to frame personal memories as a gift, in turn providing a way to revisit, share, and collate personal archives. We deployed MeMa into participants’ homes and tasked them to create a digital gift involving an autobiographical memory. Through qualitative methods we uncovered the experience of twelve gift-givers. We found that the framing as a gift brought meaning to a collection of media, promoting reflection and emotional reminiscence in participants. Our contributions include design implications involving the relationship between emotions, technology, and gifting.

In spite of research recognizing the home as a shared space and privacy as inherently social, privacy in smart homes has mainly been researched from an individual angle. Sometimes contrasting and comparing perspectives of multiple individuals, research has rarely focused on how household members might use devices communally to achieve common privacy goals. An investigation of communal use of smart home devices and its relationship with privacy in the home is lacking. The paper presents a grounded analysis based on a synergistic relationship between an ethnomethodologically-informed (EM-informed) study and a grounded theory (GT) approach. The study focuses on household members’ interactions to show that household members’ ability to coordinate the everyday use of their devices depends on appropriate conceptualizations of roles, rules, and privacy that are fundamentally different from those embodied by off-the-shelf products. Privacy is rarely an explicit, actionable, and practical consideration among household members, but rather a consideration wrapped up in everyday concerns. Roles and rules are not used to create social order, but to account for it. To sensitize to this everyday perspective and to reconcile privacy as wrapped up in everyday concerns with the design of smart home systems, the paper presents the social organization of communal use as a descriptive framework. The framework is descriptive in capturing how households navigate the ‘murky waters’ of communal use in practice, where prior research highlighted seemingly irreconcilable differences in interest, attitude, and aptitude between multiple individuals and with other stakeholders. Discussing how households’ use of roles, rules, and privacy in-practice differed from what off-the-shelf products afforded, the framework highlights critical challenges and opportunities for the design of communal privacy experiences.

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.

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.