Oudemanhuispoort, Amsterdam, Netherlands
Responsible Data Science in Law & Statistics (Event 05)
Location: Room A0.08 – Oudemanhuispoort
16:05-16:25 Speaker 1: Corien Prins, Professor of Law and Informatisation, Tilburg University on ‘What is ‘responsible’ in Responsible Data Science given law and regulation?’
16:25-16:45 Speaker 2: Peter Grünwald, Head of Machine Learning Group, Centrum voor Wiskunde en Informatica (CWI) on ‘The Reproducibility Crisis in Science: why most published research findings are wrong, and what we can do about it’
16:45-17:00 Open discussion
17:00 – Networking and drinks
17:30 – Close
On 19 January, 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