How to enable full and responsible use of big data
Responsible Data Science
Using data in reliable and responsible ways will be an integral part of any Digital Society research. Promoting responsible data science should limit the potential for misuse of personal data and the risk of undermining public trust. Techniques, methods and tools are needed to safeguard fair, accurate, confidential and transparent (FACT) use of data that is findable, accessible, interoperable and reusable (FAIR), and they should be applied universally. These and related societal challenges are addressed in programme line Responsible Data Science.
Academics that are working on finding solutions to societal challenges related to Responsible Data Science:
I am a Distinguished Professor of Data Science at Maastricht University. My research focuses on the development of computational methods for the responsible use and scalable integration of FAIR (Findable, Accessible, Interoperable and Reusable) data and services. My group combines semantic web technologies with machine learning and network analysis for drug discovery and personalized medicine. I also lead a new inter-faculty Institute of Data Science at Maastricht University whose focus is to bring together science, technology, and social, legal and ethical aspects to strengthen communities, accelerating scientific discovery, and improve health and well-being.
I seek to develop a transdisciplinary research and education programme that examines how data science and artificial intelligence can best be harnessed to tackle pressing issues in an increasingly digital society. A key part of my work will be to study data science methods to enhance multi-disciplinary collaboration, to create new streams of interdisciplinary education, and to identify effective means by which responsible data science research and innovative can be more tightly coupled for the benefit of a data science savvy society.
Frank van Harmelen is a professor in Knowledge Representation & Reasoning in the Computer Science department (Faculty of Science) at the Vrije Universiteit Amsterdam. Since 2000, he has played a leading role in the development of the Semantic Web, which aims to make data on the web semantically interpretable by machines through formal representations. He was co-PI on the first European Semantic Web project (OnToKnowledge, 1999), which laid the foundations for the Web Ontology Language OWL. OWL has become a worldwide standard, it is in wide commercial use, and has become the basis for an entire research community. In recent years, he pioneered the development of large scale reasoning engines. He was scientific director of the 10m euro EU-funded Large Knowledge Collider, a platform for distributed computation over semantic graphs with billions of edges. The prize-winning work with his student Jacopo Urbani has improved the state of the art by two orders of magnitude. He is scientific director of The Network Institute. In this interdisciplinary research institute some 150 researchers from the Faculties of Social Science, Humanities and Computer Science collaborate on research topics in computational Social Science and e-Humanities. He is a guest professor at the University of Science and Technology in Wuhan, China.
Dick den Hertog is professor of Business Analytics / Operations Research at Tilburg University. His research interests cover various fields in prescriptive analytics, in particular linear and nonlinear optimisation. In recent years his main focus has been on robust optimisation and simulation-based optimisation. He is also active in applying the theory in real-life applications. In particular, he is interested in applications that contribute to a better society. For many years he has been involved in research to optimise water safety, he is doing research to develop better optimisation models and techniques for cancer treatment, and recently he got involved in research to optimise the food supply chain for World Food Programme.
As a computer scientist trained in databases, my research has always been involved in how to effectively get meaningful information out of large datasets. Concentrating in particular on large sets of web data, most of my research has focussed on how to attaching meaning to web data, for example for the purpose of enabling web-based information systems to offer user-adapted or personalised information to their users. Being in the centre of data science, this means my research now is devoted to the theory and technology that enables developers and users of data-driven systems to trust the information that the systems provide.
With the Digital Society programme, we can increase the awareness of how data science plays a fundamental role in many of the various research efforts in studying the Digital Society. As joint universities, we can further develop the research around data science to reach that all research that applies large data sets and data science can effectively rely on the insights derived from the data and that thus all researchers that apply data science can do so in a responsible manner.
Mykola Pechenizkiy is a Full Professor at the department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e), where he holds the Data Mining Chair. His research interests include data science, knowledge discovery and data mining, responsible analytics, including ethics/discrimination-awareness, context-aware predictive analytics, handling concept drift and reoccurring contexts, automation of feature construction and analytics on evolving networks. His core expertise and research interests are in predictive analytics and knowledge discovery from evolving data, and in their application to real-world problems in industry, medicine and education. At the Data Science Center Eindhoven, he leads the Customer Journey interdisciplinary research program aiming at developing techniques for informed and responsible analytics.
David Townend is Professor of Law and Legal Philosophy in Health, Medicine and Life Sciences. His theoretical work is focused on the relationship between individual and collective responsibility in relation to health and life science research; politeness and governance; and, on the concepts of privacy and property in research governance. His practical work focuses on data protection and privacy in medical and health research, and on the creation of effective research governance.
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