9,951 research outputs found

    The State of Algorithmic Fairness in Mobile Human-Computer Interaction

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    This paper explores the intersection of Artificial Intelligence and Machine Learning (AI/ML) fairness and mobile human-computer interaction (MobileHCI). Through a comprehensive analysis of MobileHCI proceedings published between 2017 and 2022, we first aim to understand the current state of algorithmic fairness in the community. By manually analyzing 90 papers, we found that only a small portion (5%) thereof adheres to modern fairness reporting, such as analyses conditioned on demographic breakdowns. At the same time, the overwhelming majority draws its findings from highly-educated, employed, and Western populations. We situate these findings within recent efforts to capture the current state of algorithmic fairness in mobile and wearable computing, and envision that our results will serve as an open invitation to the design and development of fairer ubiquitous technologies.Comment: arXiv admin note: text overlap with arXiv:2303.1558

    Financial and Economic Review 22.

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    Facilitating prosociality through technology: Design to promote digital volunteerism

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    Volunteerism covers many activities involving no financial rewards for volunteers but which contribute to the common good. There is existing work in designing technology for volunteerism in HumanComputer Interaction (HCI) and related disciplines that focuses on motivation to improve performance, but it does not account for volunteer wellbeing. Here, I investigate digital volunteerism in three case studies with a focus on volunteer motivation, engagement, and wellbeing. My research involved volunteers and others in the volunteering context to generate recommendations for a volunteer-centric design for digital volunteerism. The thesis has three aims: 1. To investigate motivational aspects critical for enhancing digital volunteers’ experiences 2. To identify digital platform attributes linked to volunteer wellbeing 3. To create guidelines for effectively supporting volunteer engagement in digital volunteering platforms In the first case study I investigate the design of a chat widget for volunteers working in an organisation with a view to develop a design that improves their workflow and wellbeing. The second case study investigates the needs, motivations, and wellbeing of volunteers who help medical students improve their medical communication skills. An initial mixed-methods study was followed by an experiment comparing two design strategies to improve volunteer relatedness; an important indicator of wellbeing. The third case study looks into volunteer needs, experiences, motivations, and wellbeing with a focus on volunteer identity and meaning-making on a science-based research platform. I then analyse my findings from these case studies using the lens of care ethics to derive critical insights for design. The key contributions of this thesis are design strategies and critical insights, and a volunteer-centric design framework to enhance the motivation, wellbeing and engagement of digital volunteers

    Understanding the Potential of Sport for Promoting Physical Activity and Psychological Well-Being in Middle-Aged and Older Adults

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    Insufficient physical activity is considered a global public health challenge. This thesis highlights that, for middle-aged and older adults, sport participation is associated with a wide range of psychosocial benefits. Then, the thesis offers insight into the potential of walking sport programmes to promote health-enhancing physical activity in middle-aged and older adults. Recommendations are provided to promote the appeal, feasibility, and sustainability of walking sport programmes in community-based settings

    Questionnaire experience and the hybrid System Usability Scale: Using a novel concept to evaluate a new instrument

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    This article presents the concept of questionnaire experience (QX), intending to add a new element to the psychometric evaluation of questionnaires, which may eventually help increase the validity and reliability of instruments. The application of QX is demonstrated in the development of the Hybrid System Usability Scale (H-SUS), making use of items comprising pictorial and verbal elements to measure perceived usability. The H-SUS was modelled on the verbal version of the System Usability Scale (SUS). Since previous research showed advantages of pictorial scales over verbal scales (e.g., higher respondent motivation) but also disadvantages (e.g., longer completion times), we assumed that hybrid scales would combine the advantages of both scale types. The goal of this study was to compare the two instruments by assessing traditional psychometric criteria (convergent, divergent and criterion-related validity, reliability and sensitivity) and respondent-related aspects of QX (respondent workload, respondent motivation, questionnaire preference, and questionnaire completion time). An online experiment was carried out (N = 152), in which participants interacted with a smartphone prototype and subsequently completed the verbal SUS together with the H-SUS. Results indicate good psychometric properties of the H-SUS. Compared to the SUS, the H-SUS showed similar workload levels for questionnaire completion, higher levels of respondent motivation, but longer questionnaire completion time. Overall, the H-SUS is considered a promising alternative for the evaluation of perceived usability. Finally, QX can be considered a useful concept for identifying potential problems of psychometric instruments in a respondent-centred way, which may help improve the quality of future scales

    Looking before we leap: Expanding ethical review processes for AI and data science research

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    This is the final version. Available from The Ada Lovelace Institute via the DOI in this record. As part of this work, the Ada Lovelace Institute, the University of Exeter’s Institute for Data Science and Artificial Intelligence, and the Alan Turing Institute developed six mock AI and data science research proposals that represent hypothetical submissions to a Research Ethics Committee. An expert workshop found that case studies are useful training resources for understanding common AI and data science ethical challenges. Their purpose is to prompt reflection on common research ethics issues and the societal implications of different AI and data science research projects. These case studies are for use by students, researchers, members of research ethics committees, funders and other actors in the research ecosystem to further develop their ability to spot and evaluate common ethical issues in AI and data science research.Alan Turing InstituteArts and Humanities Research Counci

    Meta-ontology fault detection

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    Ontology engineering is the field, within knowledge representation, concerned with using logic-based formalisms to represent knowledge, typically moderately sized knowledge bases called ontologies. How to best develop, use and maintain these ontologies has produced relatively large bodies of both formal, theoretical and methodological research. One subfield of ontology engineering is ontology debugging, and is concerned with preventing, detecting and repairing errors (or more generally pitfalls, bad practices or faults) in ontologies. Due to the logical nature of ontologies and, in particular, entailment, these faults are often both hard to prevent and detect and have far reaching consequences. This makes ontology debugging one of the principal challenges to more widespread adoption of ontologies in applications. Moreover, another important subfield in ontology engineering is that of ontology alignment: combining multiple ontologies to produce more powerful results than the simple sum of the parts. Ontology alignment further increases the issues, difficulties and challenges of ontology debugging by introducing, propagating and exacerbating faults in ontologies. A relevant aspect of the field of ontology debugging is that, due to the challenges and difficulties, research within it is usually notably constrained in its scope, focusing on particular aspects of the problem or on the application to only certain subdomains or under specific methodologies. Similarly, the approaches are often ad hoc and only related to other approaches at a conceptual level. There are no well established and widely used formalisms, definitions or benchmarks that form a foundation of the field of ontology debugging. In this thesis, I tackle the problem of ontology debugging from a more abstract than usual point of view, looking at existing literature in the field and attempting to extract common ideas and specially focussing on formulating them in a common language and under a common approach. Meta-ontology fault detection is a framework for detecting faults in ontologies that utilizes semantic fault patterns to express schematic entailments that typically indicate faults in a systematic way. The formalism that I developed to represent these patterns is called existential second-order query logic (abbreviated as ESQ logic). I further reformulated a large proportion of the ideas present in some of the existing research pieces into this framework and as patterns in ESQ logic, providing a pattern catalogue. Most of the work during my PhD has been spent in designing and implementing an algorithm to effectively automatically detect arbitrary ESQ patterns in arbitrary ontologies. The result is what we call minimal commitment resolution for ESQ logic, an extension of first-order resolution, drawing on important ideas from higher-order unification and implementing a novel approach to unification problems using dependency graphs. I have proven important theoretical properties about this algorithm such as its soundness, its termination (in a certain sense and under certain conditions) and its fairness or completeness in the enumeration of infinite spaces of solutions. Moreover, I have produced an implementation of minimal commitment resolution for ESQ logic in Haskell that has passed all unit tests and produces non-trivial results on small examples. However, attempts to apply this algorithm to examples of a more realistic size have proven unsuccessful, with computation times that exceed our tolerance levels. In this thesis, I have provided both details of the challenges faced in this regard, as well as other successful forms of qualitative evaluation of the meta-ontology fault detection approach, and discussions about both what I believe are the main causes of the computational feasibility problems, ideas on how to overcome them, and also ideas on other directions of future work that could use the results in the thesis to contribute to the production of foundational formalisms, ideas and approaches to ontology debugging that can properly combine existing constrained research. It is unclear to me whether minimal commitment resolution for ESQ logic can, in its current shape, be implemented efficiently or not, but I believe that, at the very least, the theoretical and conceptual underpinnings that I have presented in this thesis will be useful to produce more foundational results in the field

    The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

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    The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution
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