19,039 research outputs found
Human-AI Collaboration in Academic Writing: towards a Synergy Model and A Case to Include AI as a Co-Author
No full textAs generative AI systems such as ChatGPT and Gemini 2.5 become increasingly integrated into academic workflows, the question of their legitimacy, limitations, and potential in scholarly writing has become urgent. This paper presents a reflexive case study of a sustained collaboration between a domain expert in consciousness studies and Gemini 2.5, culminating in the co-authorship of a peer-reviewed research article. By analyzing exactly 37,440 words of recorded interactions, we identify patterns of synergy, including recursive refinement, conceptual amplification, and accelerated manuscript development. We argue that when guided by a knowledgeable human author, AI can act as a cognitive partner rather than a passive tool—amplifying scholarly creativity and improving efficiency without compromising academic rigor. The case supports a '1+1=3' synergy model for co-authorship, in which human steering and AI fluency converge to produce novel insights and polished output faster and more effectively than either could achieve alone. The findings advocate for a paradigm shift from prohibitive policies to the responsible, expert-guided integration of AI in academic research and writing, grounded in transparency and accountability, and present arguments for why the AI tool should be listed as a co-author despite current injunctions against such practice
AI Robotics
No full textArtificial intelligence (AI) robots can learn from their experiences, make decisions in real time, understand natural language and human gestures, and utilize computer vision to perceive and comprehend their environments. Beginning with the rudimentary concepts of AI, AI Robotics: Ethics, Algorithms, and Technology of Artificial Intelligence-Powered Robots explores the intersection of robotics and physics and emphasizes the need for strict adherence to ethical principles in relation to overall progress and the development of humankind. Chapters on robots capable of talking, listening, and visual perception similar to human beings are followed by discussions of those that display emotional intelligence. This book also discusses task and motion planning, a set of methods that help robot hardware achieve high-level goals by breaking down tasks into smaller, more manageable steps. Lastly, the text describes autonomous robots that can make independent decisions and execute tasks on their own, utilizing sensors and AI-enabled software programmed with predefined guidelines and data. Examples of autonomous robots are presented in a chapter on robot swarms that operate in a decentralized, self-organizing manner through local communication to manage disaster relief, search-and-rescue operations, warehouse logistics, agricultural practices, and environmental exploration. Offering an up-to-date, expansive, and comprehensive treatment of the vast interdisciplinary field of AI robotics, this book will be an invaluable resource for postgraduate and doctorate students as well as academic researchers and professional engineers working on AI-enabled robotics. The electronic version of this book was funded to publish Open Access through Taylor & Francis’ Pledge to Open, a collaborative funding open access books initiative. The full list of pledging institutions can be found on the Taylor & Francis Pledge to Open webpage. Key Features Explores the research frontiers and advancements leveraged by integrating AI with robotics Highlights the unique challenges faced in robot vision and speech recognition vis-à-vis computer vision and standard speech processing Provides a state-of-the-art overview of emotional recognition, task and motion planning, and coordinated functioning of robots in multi-robot system
Is explainable AI responsible AI?
No full textWhen artificial intelligence (AI) is used to make high-stakes decisions, some worry that this will create a morally troubling responsibility gap—that is, a situation in which nobody is morally responsible for the actions and outcomes that result. Since the responsibility gap might be thought to result from individuals lacking knowledge of the future behavior of AI systems, it can be and has been suggested that deploying explainable artificial intelligence (XAI) techniques will help us to avoid it. These techniques provide humans with certain forms of understanding of the systems in question. In this paper, I consider whether existing XAI techniques can indeed close the responsibility gap. I identify a number of significant limits to their ability to do so. Ensuring that responsibility for AI-assisted outcomes is maintained may require using different techniques in different circumstances, and potentially also developing new techniques that can avoid each of the issues identified.</p
Real Stupidity: Comedians Design AI
No full textThis project involved researching the process of imagining and inventing plausible fictional AI gadgets using processes from comedy. It was a BRAID UK (Bridging Responsible Artificial Intelligence Divides) Artist Commission 2025. I worked with 4 comedians Ella Golt, John Luke Roberts, Ben Target and Frankie Thompson to make a series of video adverts advertising our 'inventions' and these were shown on a bespoke touchscreen at Inspace Gallery Edinburgh, 7-31 August 2025. The videos were part of an installation of watercolour diagrams with a publication co-created with researcher Rebecca Edwards contextualising comedy-led ideas in relation to existing/future real tech. The process was informed by participatory workshops including at BRAID UK's conference at The Lowry in Salford, and at Inspace Edinburgh during the show itself. The workshops involved participants using talking and intuitive painting to get to the crux of problems in life/society which they invented solutions to in the form of painted 'prototypes' for experimental AI. Videography was by Hannah Taylor.
Exhibited 7-31 August 2025
Tipping Point: Artist Responses to AI – at Inspace Gallery Edinburgh. The exhibition featured newly commissioned artworks from Louise Ashcroft, Julie Freeman, Wesley Goatley, Identity 2.0, Rachel Maclean, Kiki Shervington-White, and Studio Above & Below. Tipping Point explores what artists can do to help us more wisely respond to the present realities and near-future horizons of Artificial Intelligence (AI).
Each artwork presents new ways of thinking about today’s AI, the futures we want and the communities needed to build it.
This art commissioning programme is funded by the Arts and Humanities Research Council (AHRC) and delivered by BRAID in partnership with Inspace at the Institute for Design Informatics, with support from Edinburgh Art Festival and Better Images of AI
Improving Trustworthiness of AI Solutions: A Qualitative Approach to Support Ethically-Grounded AI Design
No full textPublisher Copyright: © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Despite recent efforts to make AI systems more transparent, a general lack of trust in such systems still discourages people and organizations from using or adopting them. In this article, we first present our approach for evaluating the trustworthiness of AI solutions from the perspectives of end-user explainability and normative ethics. Then, we illustrate its adoption through a case study involving an AI recommendation system used in a real business setting. The results show that our proposed approach allows for the identification of a wide range of practical issues related to AI systems and further supports the formulation of improvement opportunities and generalized design principles. By linking these identified opportunities to ethical considerations, the overall results show that our approach can support the design and development of trustworthy AI solutions and ethically-aligned business improvement.Peer reviewe
Meaningful human control: actionable properties for AI system development
No full textHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.Interactive IntelligenceDesign AestheticsCyber SecurityHuman-Robot InteractionEthics & Philosophy of TechnologyHuman Information Communication DesignWeb Information System
A Two-Dimensional Explanation Framework to Classify AI as Incomprehensible, Interpretable, or Understandable
No full textBecause of recent and rapid developments in Artificial Intelligence (AI), humans and AI-systems increasingly work together in human-agent teams. However, in order to effectively leverage the capabilities of both, AI-systems need to be understandable to their human teammates. The branch of eXplainable AI (XAI) aspires to make AI-systems more understandable to humans, potentially improving human-agent teamwork. Unfortunately, XAI literature suffers from a lack of agreement regarding the definitions of and relations between the four key XAI-concepts: transparency, interpretability, explainability, and understandability. Inspired by both XAI and social sciences literature, we present a two-dimensional framework that defines and relates these concepts in a concise and coherent way, yielding a classification of three types of AI-systems: incomprehensible, interpretable, and understandable. We also discuss how the established relationships can be used to guide future research into XAI, and how the framework could be used during the development of AI-systems as part of human-AI teams.Accepted author manuscriptInteractive Intelligenc
AI-Assisted Spirometry Interpretation in Primary Care: A Randomized Controlled Trial
No full textBACKGROUND Spirometry quality and confidence in spirometry interpretation are highly variable in primary care, contributing to underdiagnosis, overdiagnosis, and misdiagnosis of chronic respiratory diseases worldwide. Artificial intelligence (AI) decision support software has been shown to improve the accuracy of lung function interpretation in specialist care, but its applicability to primary care remains unknown. METHODS We conducted a parallel-group, randomized, controlled superiority trial to evaluate whether AI decision support software improves spirometry interpretation performance by primary care clinicians. Clinicians working in primary care who refer for, perform, or interpret spirometry assessed 50 real-world patient spirometry records, providing the most likely diagnosis (“preferred diagnosis”) through an online platform either with (intervention) or without (control) AI decision support software. The primary outcome was the preferred diagnosis prediction performance, measured as the percentage of cases in which the preferred diagnosis agreed with the reference diagnosis predetermined by expert pulmonologists. A planned subgroup analysis focused on cases with a diagnosis of chronic obstructive pulmonary disease (COPD). Secondary outcomes were performance in differential diagnosis prediction, technical quality assessment, pattern interpretation, and self-rated confidence in interpretation. RESULTS Out of 400 clinicians assessed for eligibility, 234 were randomly assigned, with 133 (57%) completing the assessment (intervention, n=67; control, n=66) — 73% female, 42% general practitioners, and 50% nurses. Compared with the control group, the addition of AI decision support software led to improvements in preferred diagnosis prediction performance in all cases (mean difference, 9.0; 95% confidence interval [CI], 4.5 to 13.3%; P=0.001) and in COPD cases (15.9; 95% CI, 9.0 to 22.7%; P<0.001). Differential diagnosis prediction and technical quality assessment performance also improved with intervention, but not pattern interpretation or clinician confidence levels. CONCLUSIONS In primary care clinicians, the adjunctive use of AI spirometry decision support software improved diagnosis prediction performance, which may help address the suboptimal interpretation of spirometry in primary care. (Funded by the National Institute for Health and Care Research and others; ClinicalTrials.gov identifier, NCT05933694.
Using Generative AI in Research
No full textThe slides accompany a workshop that is intended for graduate students to learn more about generative AI in the context of the research lifecycle. This work is licensed under a Creative Commons license so that others may share and adapt the content for other purposes as long as appropriate credit is provided to the author of the work. To access the Google slides, click here: https://bit.ly/Library_AI_Research
Learning Objectives
At the end of the session participants will be able to:
Demonstrate a basic understanding of how AI tools work
Differentiate between grounded and ungrounded AI tools
Identify key considerations for grad students/researchers
Identify ways AI tools can be used to support the phases of the research lifecycle
Identify main areas of concern with using AI tools
Outline the steps and potential resources for evaluating and citing AI outpu
Spectrum of creative agencies in AI-based art : analysis of art reviews
No full textThe relation between creativity and AI is an ongoing debate in artmaking. AI challenges the traditional understanding of who (or what) can be the creative agent and whether the outcomes are creative. Such a debate is visible in art reviews of AI-based art, but their analyses are missing from the research on AI and creativity. We analyse 39 AI art evaluations from key global newspapers to answer the following questions: how is creative agency discussed, and how are the evaluations of creative outcomes affected by the understandings of creative agency? Our results demonstrate a spectrum of creative agencies, which expands from four (human-centred, AI-centred, co-agency, assemblage) to seven (AI-assisted, AI-enchanted, and AI-improvised) agencies. Perceived creative agency is connected to the evaluation of artworks: Positive evaluations often consider human creative agency, but negative evaluations blame AI. These findings suggest that new ways to assess creativity are emerging in the AI era.© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.fi=vertaisarvioitu|en=peerReviewed
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