Name: Responsible Data Science (Event 10)
Start: 2017-10-19T16:00:00+02:00
End: 2017-10-19T18:00:00+02:00
Location: Room C147 - Faculty of Sciences - (1st Floor)

Date: Thursday 19 October 2017

Time: 16:00-18:00

Location: VU Amsterdam W&N Building - Faculty of Sciences - (1st Floor): Room C147 followed by drinks & snacks in the Intertain Lab, S111, W&N Building

16:00 Introduction & Overview on Responsible Data Science (http://www.responsibledatascience.org): Maarten de Rijke (https://staff.fnwi.uva.nl/m.derijke/) Professor in Information Retrieval, Informatics Institute, UvA.

16:05-16:25 Speaker 1: Martijn van Otterlo (http://martijnvanotterlo.nl) AAA Data Science Researcher at Knowledge, Information and Innovation (KIN), School of Business and Economics (SBE), VU.

The Ethics of Algorithms with Applications in Archives, Libraries and more.

The ethics of algorithms is becoming an important topic in a world where artificial intelligence is taking over many activities in society that were once done by humans. I see the transformation of society as being caused by digitalization and algorithmization. The first turns every once-physical activity or relation into data that contributes to a never to be erased "memory" of everything, ranging from shopping, to reading, to dating, to photographing and much more. Algorithmization then amounts to the increased employment of intelligent software that can interpret, link and analyze this data to perform super-human tasks such as predicting and influencing people at a massive scale. In this talk I will discuss some of the issues involved, drawing inspiration from from my recent work on ethical issues in (public) libraries and archives, my artificial intelligence research on a sensor-based public library project involving prediction and manipulation algorithms, and my newly constructed course on the ethics of algorithms at the Vrije Universiteit Amsterdam.

16:25-16:45 Speaker 2: Judith Möller (http://www.uva.nl/en/profile/m/o/j.e.moller1/j.e.moller.html) Political Communication & Journalism, Faculty of Social and Behavioural Sciences, UvA.

Diversity in news recommendation systems from a normative perspective.

In the debate about filter bubbles caused by algorithmic news recommendation, the conceptualization of the key issue at stake, diversity, has received little attention. In this presentation, I will present the results of a study that investigates the effect of multiple recommender systems on different diversity dimensions. To this end, I will first present a map of different values that diversity can serve, and a respective set of criteria that characterize a diverse information offer in this particular conception of diversity. The study makes use of a dataset of simulated recommendation data based on actual content of one of the major Dutch broadsheet newspapers and its users (N = 21,973 articles, N = 500 users). The study is part of the personalised-communication project and carried out in collaboration with Natali Helberger, Damian Trilling, and Bram van Es.

16:45-17:15 Speaker 3: Mykola Pechenizkiy (http://www.win.tue.nl/~mpechen/) Full Professor, Chair Data Mining, Department of Computer Science, Eindhoven University of Technology (TU/e).

FAT analytics: Facets of (un)fairness and (non)transparency

Application-driven research in predictive analytics contributes to the massive automation of the data-driven decision making and decision support. Many of these decisions affect our everyday life and its future. Data mining researchers and practitioners often have a (false) believe that data mining techniques have no bad intents. In this talk I will revisit several popular applications of predictive analytics to highlight why the general public, domain experts and policy makers have good reasons to consider off-the-shelf tools as a thread. In particular, it becomes better understood that predictive models may systematically discriminate groups of people even if data mining researchers and practitioners have only good intentions when they develop and apply predictive analytics. I will present different facets of discrimination-awareness and transparency in analytics and reflect on the current state-of-the-art and further research needed for gaining a deeper understanding of what it means for predictive analytics to be ethics-aware, transparent and accountable.

17:15-17:20 Wrap-up

17:20 – Networking and drinks in the Intertain Lab, W&N Building S111

18:00 – Close

***

On 19 October, we will hold our regular Responsible Data Science (RDS) seminar series, 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 (http://www.responsibledatascience.org)

Please note all our Meet-up events are open to all to attend, just register on our Meet-up page and RSVP to the specific event you would like to attend.

Eva Kenny

Amsterdam AI (AAI) Meetup

Technology

Computer Programming

Robotics

Business Analytics

Artificial Intelligence

Machine Learning

Business Intelligence

Big Data

Data Visualization

Data Analytics

Predictive Analytics

Data Mining

Data Science

Deep Learning

Responsible Data Science (Event 10)

Room C147 - Faculty of Sciences - (1st Floor)

Share

Amsterdam AI (AAI) Meetup

Responsible Data Science (Event 10)

Amsterdam AI (AAI) Meetup

Details

Members are also interested in