Responsible Data Science Seminar Series – Event 03
On 17 November, we will hold our third event of 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
Date: Thursday 17 November
Location: Close to Amsterdam Zuid train station
Room – Amsterdam/Moscow, Symphony Building, Gustav Mahlerplein 117, 1082 MS Amsterdam
16:05-16:25 Speaker 1:
Presentation Title: “Legal challenges of doing responsible data research, and what to expect from the new Data Protection Regulation”
16:25-16:45 Speaker 2:
Presentation Title: “Dynamic modelling of provenance and perspectives of data”
In my presentation I will present our model for source perspectives on knowledge and information that we apply in the QuPiD2 project. Quality and Perspectives in Deep Data entails three interdisciplinary projects, embedded in the Humanities and Computer Science research groups of VU and UvA (Representation of Data Perspectives, Representation of Data Quality, From Text to Deep Data).
16:45-17:00 Open discussion
17:00 – Networking and drinks
17:30 – Close
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.