Responsible Data Science Seminar Series – Event 04

On 15 December, we will hold our our monthly seminar series on Responsible Data Science (RDS), a joint collaboration of expert researchers from 11 knowledge institutions across the Netherlands.

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.

For more information see the Responsible Data Science website
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Date: Thursday 15 December

Time: 16:00-17:30

Location: Room – Amsterdam/Moscow, Symphony Building, Gustav Mahlerplein 117, 1082 MS Amsterdam

16:00-16:05 Introduction & Overview on Responsible Data Science

16:05-16:25 Speaker 1: Jack van Wijk, Professor in Visualization, Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e)

16:25-16:45 Speaker 2: Arno Lodder, Professor of Internet Governance and Regulation, Head of Department Transnational Legal Studies, Vrije Universiteit Amsterdam

16:45-17:00 Open discussion

17:00 – Networking and drinks

17:30 – Close

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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.