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:

Expertise

Dumontier is a Distinguished Professor of Data Science at Maastricht University. His 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. His group combines semantic web technologies with machine learning and network analysis for drug discovery and personalized medicine. Dumontier also leads 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.

Expertise

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.

Expertise

As a computer scientist trained in databases, Houben’s 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 his 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 his 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.

Expertise

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.

Expertise

Corien Prins is Professor of Law and Information Technology at Tilburg Law School and was president of the Tilburg Institute of Law, Technology, and Society (TILT). Since 2017, she is Chair of the Netherlands Scientific Council for Government Policy (WRR). Since 2009 she has been a member of the Royal Netherlands Academy of Arts and Sciences (KNAW), Chair of the Supervisory Board of Erasmus University Rotterdam and a member of the selection Advisory Committee of the parquet of the Supreme Court.

Expertise

Linnet Taylor is Assistant Professor of Data Ethics, Law and Policy at the Tilburg Institute for Law, Technology, and Society (TILT). Her research focuses on global data justice – the development of a conceptual framework for the ethical and beneficial governance of data technologies based on insights from technology users and providers around the world.

Examples

600 Experts in Artificial Intelligence Sign a Letter Calling for European Action

Earlier this year, 600 leading experts in artificial intelligence released a letter calling on European and national leaders to drastically ramp up their support for research excellence and innovation in artificial intelligence (AI).
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Five of the scariest predictions about artificial intelligence

As AI comes increasingly closer to maturity, and businesses continue to ramp up investments in it, some worry that not enough attention is being paid to the broader social and moral implications of the technology. CNBC spoke with some experts to see what they think are the five scariest potential future scenarios for AI. "Any computer system, AI or not, that automatically decides on matters of life and death — for example, by launching a missile — is a really scary idea".
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The future of data in academia

“Throughout the university, researchers love data and do all kind of interesting things with it”, says Michel Dumontier, distinguished professor of Data Science. “Data science is practised in every department and faculty. And yet, even though we share these methods, we barely get to talk to one another.
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