Responsible Data Science in Software Security & Computational Social Science (6)
Location: Doelenzaal, UvA Library, Singel 425 (Room C0.07 – Doelenzaal, Building C, Ground Floor)
16:00-16:05 Introduction & Overview on Responsible Data Science by Wil van der Aalst
16:05-16:25 Speaker 1: Bart Jacobs, Professor of Software Security and Correctness, Radboud University Nijmegen will present: “Polymorphic Encryption and Pseudonymisation (PEP) for medical research”
16:25-16:45 Speaker 2: Dirk Helbing, Professor of Computational Social Science, ETH Zurich will present: “On good and bad ways of using data”
16:45-17:00 Open discussion
17:00 – Networking and drinks
17:30 – Close
On 16 February, we will hold our monthly seminar series on Responsible Data Science (RDS), a joint collaboration of expert researchers from 11 knowledge institutions across the Netherlands: Academisch Medisch Centrum (AMC), Centrum Wiskunde en Informatica (CWI), Delft University of Technology (TUD), Eindhoven University of Technology (TU/e), Leiden University (LU), Leiden University Medical Center (LUMC), Radboud University Nijmegen (RU), Tilburg University (UvT), University of Amsterdam (UvA), VU Medical Center Amsterdam (VUmc), VU University Amsterdam.
The RDS initiative is driven by the omnipresence of data making society increasingly dependent on data science. Despite its great potential, there are also many concerns on irresponsible data use. Unfair or biased conclusions, disclosure of private information, and non-transparent data use, may inhibit future data science applications.
The RDS programme aims at generating scientific breakthroughs by making data science responsible by design. In RDS researchers from multiple disciplines connect to develop techniques, tools, and approaches to ensure fairness, accuracy, confidentiality, and transparency.
Big data is changing the way we do business, socialize, conduct research, and govern society. Data are collected on anything, at any time, and in any place. Organizations are investing heavily in Big data technologies and data science has emerged as a new scientific discipline providing techniques, methods, and tools to gain value and insights from new and existing data sets. Data abundance combined with powerful data science techniques has the potential to dramatically improve our lives by enabling new services and products, while improving their efficiency and quality. Many of today’s scientific discoveries (e.g., in health) are fuelled by developments in statistics, data mining, machine learning, databases, and visualization.
For more information see the Responsible Data Science website HERE