Data Processing Model for Compliance with International Medical Research Data Processing Rules

In addition to traditional clinical research, advances in information communication technologies facilitates new medical research using internet of things devices and other cutting-edge technologies. Such medical research also simplifies the collection of data on research subjects in their daily lives internationally. In this context, medical research is increasingly required to comply with rules protecting patients’ personal data. This study proposes a model to enable researchers and other stakeholders including ethics committees in such international medical research to easily verify whether the planned processing of patient data complies with relevant legal and ethical rules. The model proposed in this study consists of (1) how patient information is pro-cessed, (2) the rules that are relevant to the processing, and (3) the analysis of whether the processing complies with the rules. This study suggests that the model should describe the aspects of data processing that are sub-ject to many rules, such as the location of the processing, categories of data, purposes of the processing, and the storage period. Thus, using the information described in the model as a guide, stakeholders can determine which national and international legal/ethical rules apply to the planned processing. Then, they can use the model to verify and document whether the processing complies with the specific regulatory rules. The use of the model in this study enables stakeholders in medical research to comply with the rules related to patient data more effectively than without using the model

Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning: How can the output of AI systems be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

Who is Reading Whom Now: Privacy in Education from Books to MOOCs

This Article is the most comprehensive study to date of the policy issues and privacy concerns arising from the surge of ed tech innovation. It surveys the burgeoning market of ed tech solutions, which range from free Android and iPhone apps to comprehensive learning management systems and digitized curricula delivered via the Internet. It discusses the deployment of big data analytics by education institutions to enhance student performance, evaluate teachers, improve education techniques, customize programs, and better leverage scarce resources to optimize education results. This Article seeks to untangle ed tech privacy concerns from the broader policy debates surrounding standardization, the Common Core, longitudinal data systems, and the role of business in education. It unpacks the meaning of commercial data uses in schools, distinguishing between behavioral advertising to children and providing comprehensive, optimized education solutions to students, teachers, and school systems. It addresses privacy problems related to small data --the individualization enabled by optimization solutions that read students even as they read their books-as well as concerns about big data analysis and measurement, including algorithmic biases, discreet discrimination, narrowcasting, and chilling effects. This Article proposes solutions ranging from deployment of traditional privacy tools, such as contractual and organizational governance mechanisms, to greater data literacy by teachers and parental involvement. It advocates innovative technological solutions, including converting student data to a parent-accessible feature and enhancing algorithmic transparency to shed light on the inner working of the machine. For example, individually curated data backpacks would empower students and their parents by providing them with comprehensive portable profiles to facilitate personalized learning regardless of where they go. This Article builds on a methodology developed in the authors\u27 previous work to balance big data rewards against privacy risks, while complying with several layers of federal and state regulation

Conceptual Modeling in Law: An Interdisciplinary Research Agenda

The article describes how different approaches from the IS field of conceptual modeling should be transferred to the legal domain to enhance comprehensibility of legal regulations and contracts. It is further described how this in turn would benefit the IS discipline. The findings emphasize the importance of further interdisciplinary research on that topic. A research agenda that synthesizes the presented ideas is proposed based on a framework that structures the research field. Researchers from both disciplines, IS and Law, that are interested in this field should use the research agenda to position their research and to derive new and innovative research questions

Communication and Community: The Impact of Closed Facebook Groups on Athletic Trainers

This qualitative research study will use semi-structured interviews to explore the motivations of athletic trainers using social media for professional collaboration. The socio-psychological tradition is used to frame the study. Communities of practice, uses and gratification theory, and the Wisdom of the Crowd model are used as the guiding theoretical perspectives as they provide a framework for understanding how and why athletic trainers use social media. This qualitative study sought to show the impact of closed Facebook Group participation on athletic trainers. How athletic trainers understand patient privacy laws when participating in closed Facebook groups for athletic trainers will also be explored

Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination

Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives

Governing Privacy in Knowledge Commons

Scholars from various disciplines explore privacy governance using the Governing Knowledge Commons framework. Case studies drawn from contexts such as academia, social media, mental health, and IoT provide insights into how privacy shapes community knowledge production. This title is also available as Open Access on Cambridge Core