259 research outputs found
Trustworthy journalism through AI
Quality journalism has become more important than ever due to the need for quality and trustworthy media outlets that can provide accurate information to the public and help to address and counterbalance the wide and rapid spread of disinformation. At the same time, quality journalism is under pressure due to loss of revenue and competition from alternative information providers. This vision paper discusses how recent advances in Artificial Intelligence (AI), and in Machine Learning (ML) in particular, can be harnessed to support efficient production of high-quality journalism. From a news consumer perspective, the key parameter here concerns the degree of trust that is engendered by quality news production. For this reason, the paper will discuss how AI techniques can be applied to all aspects of news, at all stages of its production cycle, to increase trust
Managing healthcare transformation towards P5 medicine (Published in Frontiers in Medicine)
Health and social care systems around the world are facing radical organizational, methodological and technological paradigm changes to meet the requirements for improving quality and safety of care as well as efficiency and efficacy of care processes. In this they’re trying to manage the challenges of ongoing demographic changes towards aging, multi-diseased societies, development of human resources, a health and social services consumerism, medical and biomedical progress, and exploding costs for health-related R&D as well as health services delivery. Furthermore, they intend to achieve sustainability of global health systems by transforming them towards intelligent, adaptive and proactive systems focusing on health and wellness with optimized quality and safety outcomes.
The outcome is a transformed health and wellness ecosystem combining the approaches of translational medicine, 5P medicine (personalized, preventive, predictive, participative precision medicine) and digital health towards ubiquitous personalized health services realized independent of time and location. It considers individual health status, conditions, genetic and genomic dispositions in personal social, occupational, environmental and behavioural context, thus turning health and social care from reactive to proactive. This requires the advancement communication and cooperation among the business actors from different domains (disciplines) with different methodologies, terminologies/ontologies, education, skills and experiences from data level (data sharing) to concept/knowledge level (knowledge sharing). The challenge here is the understanding and the formal as well as consistent representation of the world of sciences and practices, i.e. of multidisciplinary and dynamic systems in variable context, for enabling mapping between the different disciplines, methodologies, perspectives, intentions, languages, etc. Based on a framework for dynamically, use-case-specifically and context aware representing multi-domain ecosystems including their development process, systems, models and artefacts can be consistently represented, harmonized and integrated. The response to that problem is the formal representation of health and social care ecosystems through an system-oriented, architecture-centric, ontology-based and policy-driven model and framework, addressing all domains and development process views contributing to the system and context in question.
Accordingly, this Research Topic would like to address this change towards 5P medicine. Specifically, areas of interest include, but are not limited:
• A multidisciplinary approach to the transformation of health and social systems
• Success factors for sustainable P5 ecosystems
• AI and robotics in transformed health ecosystems
• Transformed health ecosystems challenges for security, privacy and trust
• Modelling digital health systems
• Ethical challenges of personalized digital health
• Knowledge representation and management of transformed health ecosystems
Table of Contents:
04 Editorial: Managing healthcare transformation towards P5
medicine
Bernd Blobel and Dipak Kalra
06 Transformation of Health and Social Care Systems—An
Interdisciplinary Approach Toward a Foundational
Architecture
Bernd Blobel, Frank Oemig, Pekka Ruotsalainen and Diego M. Lopez
26 Transformed Health Ecosystems—Challenges for Security,
Privacy, and Trust
Pekka Ruotsalainen and Bernd Blobel
36 Success Factors for Scaling Up the Adoption of Digital
Therapeutics Towards the Realization of P5 Medicine
Alexandra Prodan, Lucas Deimel, Johannes Ahlqvist, Strahil Birov,
Rainer Thiel, Meeri Toivanen, Zoi Kolitsi and Dipak Kalra
49 EU-Funded Telemedicine Projects – Assessment of, and
Lessons Learned From, in the Light of the SARS-CoV-2
Pandemic
Laura Paleari, Virginia Malini, Gabriella Paoli, Stefano Scillieri,
Claudia Bighin, Bernd Blobel and Mauro Giacomini
60 A Review of Artificial Intelligence and Robotics in
Transformed Health Ecosystems
Kerstin Denecke and Claude R. Baudoin
73 Modeling digital health systems to foster interoperability
Frank Oemig and Bernd Blobel
89 Challenges and solutions for transforming health ecosystems
in low- and middle-income countries through artificial
intelligence
Diego M. López, Carolina Rico-Olarte, Bernd Blobel and Carol Hullin
111 Linguistic and ontological challenges of multiple domains
contributing to transformed health ecosystems
Markus Kreuzthaler, Mathias Brochhausen, Cilia Zayas, Bernd Blobel
and Stefan Schulz
126 The ethical challenges of personalized digital health
Els Maeckelberghe, Kinga Zdunek, Sara Marceglia, Bobbie Farsides
and Michael Rigb
Ethnographically-informed distributed participatory design framework for sociotechnical change : co-designing a collaborative training tool to support real-time collaborative writing
Although Wikipedia’s immense success is partially due to its support of the asynchronous collaboration model, researchers argue that the bureaucratic rules and technical infrastructure enabling it feed into Wikipedia’s content bias. Attempts to introduce different collaboration models have so far failed, but the fact that they have occurred persistently over time suggests that at least part of the Wikipedia community favours incorporating features such as real-time collaborative editing.
My research is founded on the argument that the advantageous aspects of the asynchronous model should be preserved, although the existing model needs to be complemented by real-time collaboration in settings such as Wikipedia training events. This thesis describes a Participatory Design process resulting in a prototype called WikiSync, a system that introduces real-time collaboration for the Wikipedia community using a responsible design approach that is respectful of Wikipedia’s rich social structure and history.
Furthermore, my research has produced an adaptive methodology for co-designing sociotechnical solutions in a geographically distributed community. After an in-depth observation of online Wikipedia training and the existing community innovation processes, my participatory design sessions have helped create a mutual learning environment for co-designing WikiSync in tandem with the community, while addressing a wide range of their concerns about real-time collaboration. I also consulted the broader Wikipedia community using an online social ideation and voting tool to evaluate the desirability and applicability of the solution. Finally, the resulting ethnographically-informed distributed Participatory Design framework provides an innovation process for involving a diverse, widely distributed online community in co-designing sociotechnical solutions
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Ensuring alignment, which refers to making models behave in accordance with
human intentions [1,2], has become a critical task before deploying large
language models (LLMs) in real-world applications. For instance, OpenAI devoted
six months to iteratively aligning GPT-4 before its release [3]. However, a
major challenge faced by practitioners is the lack of clear guidance on
evaluating whether LLM outputs align with social norms, values, and
regulations. This obstacle hinders systematic iteration and deployment of LLMs.
To address this issue, this paper presents a comprehensive survey of key
dimensions that are crucial to consider when assessing LLM trustworthiness. The
survey covers seven major categories of LLM trustworthiness: reliability,
safety, fairness, resistance to misuse, explainability and reasoning, adherence
to social norms, and robustness. Each major category is further divided into
several sub-categories, resulting in a total of 29 sub-categories.
Additionally, a subset of 8 sub-categories is selected for further
investigation, where corresponding measurement studies are designed and
conducted on several widely-used LLMs. The measurement results indicate that,
in general, more aligned models tend to perform better in terms of overall
trustworthiness. However, the effectiveness of alignment varies across the
different trustworthiness categories considered. This highlights the importance
of conducting more fine-grained analyses, testing, and making continuous
improvements on LLM alignment. By shedding light on these key dimensions of LLM
trustworthiness, this paper aims to provide valuable insights and guidance to
practitioners in the field. Understanding and addressing these concerns will be
crucial in achieving reliable and ethically sound deployment of LLMs in various
applications
Challenges and perspectives of hate speech research
This book is the result of a conference that could not take place. It is a collection of 26 texts that address and discuss the latest developments in international hate speech research from a wide range of disciplinary perspectives. This includes case studies from Brazil, Lebanon, Poland, Nigeria, and India, theoretical introductions to the concepts of hate speech, dangerous speech, incivility, toxicity, extreme speech, and dark participation, as well as reflections on methodological challenges such as scraping, annotation, datafication, implicity, explainability, and machine learning. As such, it provides a much-needed forum for cross-national and cross-disciplinary conversations in what is currently a very vibrant field of research
Geographic information extraction from texts
A large volume of unstructured texts, containing valuable geographic information, is available online. This information – provided implicitly or explicitly – is useful not only for scientific studies (e.g., spatial humanities) but also for many practical applications (e.g., geographic information retrieval). Although large progress has been achieved in geographic information extraction from texts, there are still unsolved challenges and issues, ranging from methods, systems, and data, to applications and privacy. Therefore, this workshop will provide a timely opportunity to discuss the recent advances, new ideas, and concepts but also identify research gaps in geographic information extraction
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