1,692 research outputs found
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
The Epidemiology and Management of Kawasaki Disease in Australia
Kawasaki disease (KD) is a syndrome of systemic inflammation with the potential to cause life-threatening aneurysms of the coronary arteries. I sought to contribute to our understanding of this important condition, particularly with regard to Australian children.
By determining the hospitalisation rate and IVIG-treatment rate I estimated the incidence of KD to be about 14 per 100,000 children under the age of 5 between 2007 and 2015. I also showed that the hospitalisation rate nationally had increased on average 3.5% annually between 1993 and 2018, with significant changes in the age distribution over that period.
In collaboration with the Paediatric Active Enhanced Disease Surveillance (PAEDS) network, I undertook a large multicentre prospective surveillance study of KD in Australia. My analysis of that cohort confirmed several of the findings from the survey, such as the preference of Australian clinicians for low-dose aspirin from the time of diagnosis, and the considerable variability around how IVIG resistance is diagnosed and managed. Importantly, I observed that a significant subset of children diagnosed with, and treated for, KD do not meet the diagnostic criteria outlined in the 2017 statement by the American Heart Association.
This work has contributed significantly to the understanding of KD’s epidemiology, management, and outcomes in Australia. I have shown that the incidence of the condition is increasing, and the clinical picture is changing. I identified important areas of practice variation and highlighted the need for international collaboration around agreed definitions (such as for IVIG resistance). Finally, I have played a central role in establishing an important resource for future resource: prospective surveillance of KD in Australia continues, with well over 700 cases recruited so far. It is hoped that this work will be of benefit to the researchers, clinicians, patients, and families affected by KD now, and into the future
“So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy
Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts
Reframing museum epistemology for the information age: a discursive design approach to revealing complexity
This practice-based research inquiry examines the impact of an epistemic shift, brought about by the dawning of the information age and advances in networked communication technologies, on physical knowledge institutions - focusing on museums. The research charts adapting knowledge schemas used in museum knowledge organisation and discusses the potential for a new knowledge schema, the network, to establish a new epistemology for museums that reflects contemporary hyperlinked and networked knowledge. The research investigates the potential for networked and shared virtual reality spaces to reveal new ‘knowledge monuments’ reflecting the epistemic values of the network society and the space of flows.
The central practice for this thesis focuses on two main elements. The first is applying networks and visual complexity to reveal multi-linearity and adapting perspectives in relational knowledge networks. This concept was explored through two discursive design projects, the Museum Collection Engine, which uses data visualisation, cloud data, and image recognition within an immersive projection dome to create a dynamic and searchable museum collection that returns new and interlinking constellations of museum objects and knowledge. The second discursive design project was Shared Pasts: Decoding Complexity, an AR app with a unique ‘anti-personalisation’ recommendation system designed to reveal complex narratives around historic objects and places. The second element is folksonomy and co-design in developing new community-focused archives using the community's language to build the dataset and socially tagged metadata. This was tested by developing two discursive prototypes, Women Reclaiming AI and Sanctuary Stories
IMAGINING, GUIDING, PLAYING INTIMACY: - A Theory of Character Intimacy Games -
Within the landscape of Japanese media production, and video game production in particular, there is a niche comprising video games centered around establishing, developing, and fulfilling imagined intimate relationships with anime-manga characters. Such niche, although very significant in production volume and lifespan, is left unexplored or underexplored. When it is not, it is subsumed within the scope of wider anime-manga media. This obscures the nature of such video games, alternatively identified with descriptors including but not limited to ‘visual novel’, ‘dating simulator’ and ‘adult computer game’.
As games centered around developing intimacy with characters, they present specific ensembles of narrative content, aesthetics and software mechanics. These ensembles are aimed at eliciting in users what are, by all intents and purposes, parasocial phenomena towards the game’s characters. In other words, these software products encourage players to develop affective and bodily responses towards characters. They are set in a way that is coherent with shared, circulating scripts for sexual and intimate interaction to guide player imaginative action. This study defines games such as the above as ‘character intimacy games’, video game software where traversal is contingent on players knowingly establishing, developing, and fulfilling intimate bonds with fictional characters. To do so, however, player must recognize themselves as playing that type of game, and to be looking to develop that kind of response towards the game’s characters. Character Intimacy Games are contingent upon player developing affective and bodily responses, and thus presume that players are, at the very least, non-hostile towards their development. This study approaches Japanese character intimacy games as its corpus, and operates at the intersection of studies of communication, AMO studies and games studies.
The study articulates a research approach based on the double need of approaching single works of significance amidst a general scarcity of scholarly background on the subject. It juxtaposes data-driven approaches derived from fan-curated databases – The Visual Novel Database and Erogescape -Erogē Hyōron Kūkan – with a purpose-created ludo-hermeneutic process. By deploying an observation of character intimacy games through fan-curated data and building ludo-hermeneutics on the resulting ontology, this study argues that character intimacy games are video games where traversal is contingent on players knowingly establishing, developing, and fulfilling intimate bonds with fictional characters and recognizing themselves as doing so. To produce such conditions, the assemblage of software mechanics and narrative content in such games facilitates intimacy between player and characters. This is, ultimately, conductive to the emergence of parasocial phenomena. Parasocial phenomena, in turn, are deployed as an integral assumption regarding player activity within the game’s wider assemblage of narrative content and software mechanics
Explainable temporal data mining techniques to support the prediction task in Medicine
In the last decades, the increasing amount of data available in all fields raises the necessity to discover new knowledge and explain the hidden information found. On one hand, the rapid increase of interest in, and use of, artificial intelligence (AI) in computer applications has raised a parallel concern about its ability (or lack thereof) to provide understandable, or explainable, results to users. In the biomedical informatics and computer science communities, there is considerable discussion about the `` un-explainable" nature of artificial intelligence, where often algorithms and systems leave users, and even developers, in the dark with respect to how results were obtained. Especially in the biomedical context, the necessity to explain an artificial intelligence system result is legitimate of the importance of patient safety. On the other hand, current database systems enable us to store huge quantities of data. Their analysis through data mining techniques provides the possibility to extract relevant knowledge and useful hidden information. Relationships and patterns within these data could provide new medical knowledge. The analysis of such healthcare/medical data collections could greatly help to observe the health conditions of the population and extract useful information that can be exploited in the assessment of healthcare/medical processes. Particularly, the prediction of medical events is essential for preventing disease, understanding disease mechanisms, and increasing patient quality of care. In this context, an important aspect is to verify whether the database content supports the capability of predicting future events. In this thesis, we start addressing the problem of explainability, discussing some of the most significant challenges need to be addressed with scientific and engineering rigor in a variety of biomedical domains. We analyze the ``temporal component" of explainability, focusing on detailing different perspectives such as: the use of temporal data, the temporal task, the temporal reasoning, and the dynamics of explainability in respect to the user perspective and to knowledge. Starting from this panorama, we focus our attention on two different temporal data mining techniques. The first one, based on trend abstractions, starting from the concept of Trend-Event Pattern and moving through the concept of prediction, we propose a new kind of predictive temporal patterns, namely Predictive Trend-Event Patterns (PTE-Ps). The framework aims to combine complex temporal features to extract a compact and non-redundant predictive set of patterns composed by such temporal features. The second one, based on functional dependencies, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework. We then discuss the concept of approximation, the data complexity of deriving an APFD, the introduction of two new error measures, and finally the quality of APFDs in terms of coverage and reliability. Exploiting these methodologies, we analyze intensive care unit data from the MIMIC dataset
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