This article explores the multifaceted relationship between social media use and individual wellbeing (SM-WB). It focuses on personality traits, locus of control, social media competency, and cultural backgrounds. An online survey was conducted with 281 Arabs (141 females) and 281 British (155 females). Analyses revealed significant differences: Arabs exhibited higher belief in social media's positive impact on wellbeing, consistent across various wellbeing dimensions measured through a customized PERMA scale. Regression analysis identified significant predictors of SM-WB—social media competency, conscientiousness, agreeableness, neuroticism, and internal locus of control—for both samples, while females reported higher SM-WB than males. Age was a significant predictor exclusively in the British sample, whereas extraversion predicted SM-WB only in the Arab sample. Qualitative findings regarding future social media design to enhance wellbeing revealed several similarities between samples; however, some themes differed in their specifics, underscoring culture’s nuanced impact on social media developmental requirements for improving wellbeing.

Formal Modelling is often used as part of the design and testing process of software development to ensure that components operate within suitable bounds even in unexpected circumstances. We conducted a user study evaluation of predictive formal modelling (PFM) at runtime in a human-swarm mission to determine the benefit of PFM on performance and human-swarm interaction. A total of 180 participants were recruited to perform the role of aerial swarm operators delivering parcels to target locations in a simulation environment. The PFM model was integrated into the simulation software to inform the operator of the estimated mission completion time given the current number of drones deployed. The operator could increase the number of parcels delivered in any timestep by adding drones, which also increased costs, thus requiring the use of the minimum number of drones necessary to complete the task in the given time. We collected user feedback using standard survey questionnaires and measured performance using data obtained from the Human and Robot Interactive Swarm (HARIS) simulator. Our results show that PFM increased the performance of the human swarm team without significantly increasing the operators’ workload or affecting the system’s usability.

The global economy is rapidly contracting, and as more countries experience recessions, it is crucial to have reliable tools for predicting them and figuring out the variables that influence how well an economy is doing. Twitter and other social media platforms have become important informational resources for forecasting market movements and identifying potential future threats. In this study, sentiment analysis is performed on real-time Twitter data to forecast a recession for the UK economy. Twitter feeds on recession are fed into Azure to pre-processes the data, and to produce insights on the recession’s contributing factors. A model to accurately forecast the recession using a Graph Neural Network (GNN) is created on the processed data. The main contribution of the research is the creation of a framework for forecasting the UK recession using GNN on realtime Twitter data. The purpose of the study is to give insights into the variables influencing the UK’s economic health and to pinpoint reliable recession forecasting techniques. Policymakers, economists, and companies wishing to track the UK economy in real time may find the research findings interesting. Overall, the work has important ramifications for forecasting recessions and keeping tabs on economic circumstances utilising Twitter, GNN, and Azure Databricks.

Despite the advantages of having robot swarms, human supervision is required for real-world applications. The performance of the human-swarm system depends on several factors including the data availability for the human operators. In this paper, we study the human factors aspect of the human-swarm interaction and investigate how having access to high-quality data can affect the performance of the human-swarm system— the number of tasks completed and the human trust level in operation. We designed an experiment where a human operator is tasked to operate a swarm to identify casualties in an area within a given time period. One group of operators had the option to request high-quality pictures while the other group had to base their decision on the available low-quality images. We performed a user study with 120 participants and recorded their success rate (directly logged via the simulation platform) as well as their workload and trust level (measured through a questionnaire after completing a human-swarm scenario). The findings from our study indicated that the group granted access to high-quality data exhibited an increased workload and placed greater trust in the swarm, thus confirming our initial hypothesis. However, we also found that the number of accurately identified casualties did not significantly vary between the two groups, suggesting that data quality had no impact on the successful completion of tasks

Machine learning has made rapid advances in safety-critical applications, such as traffic control, finance, and healthcare. With the criticality of decisions they support and the potential consequences of following their recommendations, it also became critical to provide users with explanations to interpret machine learning models in general, and black-box models in particular. However, despite the agreement on explainability as a necessity, there is little evidence on how recent advances in eXplainable Artificial Intelligence literature (XAI) can be applied in collaborative decision-making tasks, i.e., human decision-maker and an AI system working together, to contribute to the process of trust calibration effectively. This research conducts an empirical study to evaluate four XAI classes for their impact on trust calibration. We take clinical decision support systems as a case study and adopt a within-subject design followed by semi-structured interviews. We gave participants clinical scenarios and XAI interfaces as a basis for decision-making and rating tasks. Our study involved 41 medical practitioners who use clinical decision support systems frequently. We found that users perceive the contribution of explanations to trust calibration differently according to the XAI class and to whether XAI interface design fits their job constraints and scope. We revealed additional requirements on how explanations shall be instantiated and designed to help a better trust calibration. Finally, we build on our findings and present guidelines for designing XAI interfaces.

Recent advances in artificial intelligence, specifically machine learning, contributed positively to enhancing the autonomous systems industry, along with introducing social, technical, legal and ethical challenges to make them trustworthy. Although Trustworthy Autonomous Systems (TAS) is an established and growing research direction that has been discussed in multiple disciplines, e.g., Artificial Intelligence, Human-Computer Interaction, Law, and Psychology. The impact of TAS on education curricula and required skills for future TAS engineers has rarely been discussed in the literature. This study brings together the collective insights from a number of TAS leading experts to highlight significant challenges for curriculum design and potential TAS required skills posed by the rapid emergence of TAS. Our analysis is of interest not only to the TAS education community but also to other researchers, as it offers ways to guide future research toward operationalising TAS education.

Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent for human decision-makers. An essential requirement for their success is the ability to provide explanations about themselves that are understandable and meaningful to the users. While explanations generally have positive connotations, studies showed that the assumption behind users interacting and engaging with these explanations could introduce trust calibration errors such as facilitating irrational or less thoughtful agreement or disagreement with the AI recommendation. In this paper, we explore how to help trust calibration through explanation interaction design. Our research method included two main phases. We first conducted a think-aloud study with 16 participants aiming to reveal main trust calibration errors concerning explainability in AI-Human collaborative decision-making tools. Then, we conducted two co-design sessions with eight participants to identify design principles and techniques for explanations that help trust calibration. As a conclusion of our research, we provide five design principles: Design for engagement, challenging habitual actions, attention guidance, friction and support training and learning. Our findings are meant to pave the way towards a more integrated framework for designing explanations with trust calibration as a primary goal.