Persuasive interfaces raise ethical concerns when users are unaware of persuasion or find it hard to resist it. Inoculation Theory suggests that attitudes can be inoculated against persuasive attacks. Studies show that disclosure statements in native advertising help people recognize persuasive intent. Likewise, just-in-time disclosure statements in persuasive interfaces may have a similar effect. In this article, explainable persuasion was used as an inoculation intervention to build resistance against persuasive interfaces. The effectiveness of this approach was assessed via a 4x2 online experiment, taking online gambling as an illustrative domain. 240 participants (age range 18–73 years, 138 male, 100 female, 2 participants choose not to disclose) were recruited from the UK. Inoculation was delivered through an animated video, while explainable persuasion was operationalized through the disclosure of persuasive intent. The findings showed that explainable persuasion increased awareness of the presence and risks of persuasive interfaces and strengthened user resistance to persuasive attempts. Explainable persuasion, being information-based, can be a cost-effective strategy for helping people stay in control over their digital usage while engaging with persuasive technologies.

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.

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.

Many people worldwide rely on social media to satisfy their social needs for relatedness, learning and enhancing self-esteem. However, over-reliance on social media often results in problematic attachment, which risks personal, social and financial wellbeing. From a design perspective, we argue that social media can be improved with tools to manage such problematic attachment and help users reform their interaction style, social expectations and online identity to restore a healthy reliance. Designing such behaviour change tools can be challenging due to the characteristics of people with problematic behaviours, e.g. denial, relapse and cognitive dissonance. This paper explores the role of social media in such attachment and reveals associated psychological states. Our method provides an ecologically valid exploration through employing diary studies as a data collection method, aiming to introduce countermeasures for problematic attachment in future social media design.

ADAS (Advanced Driver Assistance Systems) are in-vehicle systems designed to enhance driving safety and comfort. Unlike active ADAS which provide direct intervention to avoid accidents, passive ADAS increase driver's awareness of hazardous situations by giving warnings in advance. It has been noted that these systems can cause distraction when the relevant HMIs (Human-Machine Interfaces) are poorly designed. Current research is limited to address this problem in specific settings which may not be applicable in wider context. This papers aims to provide a universal rule-based solution to allow passive ADAS to initiate warnings without triggering driver distraction through an ontological approach.

The Royal Bournemouth and Christchurch Hospitals (RBCH) use a series of paper forms to record their interactions with patients; while these have been highly successful, the world is moving digitally and the National Health Service (NHS) has planned to be completely paperless by 2020. Using a project management methodology called Scrum that is supported by a usability evaluation technique called System Usability Scale (SUS) and a workload measurement technique called NASA TLX, a prototype web application system was built and evaluated for the client. The prototype used a varied set of input mediums including voice, text and stylus to ensure that users were more likely to adopt the system. This web based system was successfully developed and evaluated at RBCH. This evaluation showed that the application was usable and accessible but raised many different questions about the nature of software in hospitals. While the project looked at how different input mediums can be used in a hospital, it found that just because it is possible to input data is some familiar format (e.g. voice), it is not always in the best interest of the end-users and the patients.

Smart phones have become the hub of people lives due to the overwhelming number and extensive range of apps available in app stores that are available to support their daily tasks. On average, smart phone users have around 100 apps installed on their devices and the number is ever growing. Thus, it becomes crucial to make sure they can quickly access these apps. In this paper, we present an exploratory study to understand users' memorability of their self-defined gestures for 15 frequently used mobile apps. The results show that although participants recalled their self-defined gestures most of the time, there are still certain factors that can influence their recall. The paper further analyses the underlining reasons and discusses how such issues could be addressed from a technical perspective.

Crowdsourcing is an emerging online paradigm for problem solving which involves a large number of people often recruited on a voluntary basis and given, as a reward, some tangible or intangible incentives. It harnesses the power of the crowd for minimizing costs and, also, to solve problems which inherently require a large, decentralized and diverse crowd. In this paper, we advocate the potential of crowdsourcing for software evaluation. This is especially true in the case of complex and highly variable software systems, which work in diverse, even unpredictable, contexts. The crowd can enrich and keep the timeliness of the developers' knowledge about software evaluation via their iterative feedback. Although this seems promising, crowdsourcing evaluation introduces a new range of challenges mainly on how to organize the crowd and provide the right platforms to obtain and process their input. We focus on the activity of obtaining evaluation feedback from the crowd and conduct two focus groups to understand the various aspects of such an activity. We finally report on a set of challenges to address and realize correct and efficient crowdsourcing mechanisms for software evaluation.

Software-mediated interactions introduce a new set of etiquettes not necessarily analogous to face-to-face interactions. Current software systems are not prepared to accommodate how users differ in their perception of the considerate nature of an interaction. Being socially inconsiderate affects the relationship between colleagues, their cooperation level, willingness to interact and, ultimately, the entire business. Hence, software acting on behalf of people or facilitating their interaction should be expected to support a rationale where both socialness and business goals are recognised. However, despite this vital role, the sociality of business interactions has rarely been considered within information systems development and research, even among areas that attempt to align the system with business needs. In this paper, we conduct an empirical study, following a mixed method approach, trying to explore the users' attitude and their perception of the elements of consideration in software-mediate interaction and what they would expect future software to provide in relation to consideration. We list a set of research challenges for information systems which embed consideration as a main social requirement.

Business Process Management (BPM) systems have been deployed in many large organisations to improve their business effectiveness and efficiency. Cloud-based BPM systems have provided SMEs using BPM in a pay-per-use manner. Previous work has focused on looking at Cloud-based BPM from the perspectives of distribution of data, activity or/and business engine and related issues, such as scalability of system, security of data, distribution of data and activities. To achieve business agility business process collaboration needs to seamlessly connect local BPM systems and cloud-based BPM systems. In this paper we look at BPM in the cloud from a new user perspective, how process models can be handled in the cloud for the fast pace of change of business collaborations. The paper proposes a distribution solution in which at the design time, the shared process model can be discovered from a process repository, and adapted to local needs; at runtime a process is distributed. A real world case is used to explain our design and implementation. An incident notification system collaborative process is built to work across different organisations.