17. – 22. Juli 2016, Dagstuhl-Seminar 16291

Data, Responsibly


Serge Abiteboul (ENS – Cachan, FR)
Gerome Miklau (University of Massachusetts – Amherst, US)
Julia Stoyanovich (Drexel Univ. – Philadelphia, US)
Gerhard Weikum (MPI für Informatik – Saarbrücken, DE)

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Our society is data-driven. Large scale data analysis, known as Big data, is distinctly present in the private lives of individuals, is a dominant force in commercial domains as varied as automatic manufacturing, e-commerce and personalized medicine, and assists in - or fully automates - decision making in the public and private sectors. Data-driven algorithms are used in criminal sentencing - ruling who goes free and who remains behind bars, in college admissions - granting or denying access to education, and in employment and credit decisions - offering or withholding economic opportunities.

The promise of Big data is to improve people's lives, accelerate scientific discovery and innovation, and enable broader participation. Yet, if not used responsibly, Big data can increase economic inequality and affirm systemic bias, polarize rather than democratize, and deny opportunities rather than improve access. Worse yet, all this can be done in a way that is non-transparent and defies public scrutiny.

Big data impacts individuals, groups and society as a whole. Because of the central role played by this technology, it must be used responsibly - in accordance with the ethical and moral norms that govern our society, and adhering to the appropriate legal and policy frameworks. And as journalists [3], legal and policy scholars [1,2] and governments [4,5] are calling for algorithmic fairness and greater insight into data-driven algorithmic processes, there is an urgent need to define a broad and coordinated computer science research agenda in this area. The primary goal of the Dagstuhl Seminar "Data, Responsibly" was to make progress towards such an agenda.

The seminar brought together academic and industry researchers from several areas of computer science, including a broad representation of data management, but also data mining, security/privacy, and computer networks, as well as social sciences researchers, data journalists, and those active in government think-tanks and policy initiatives. The problem we aim to address is inherently transdisciplinary. For this reason, it was important to have input from policy and legal scholars, and to have representation from multiple areas within computer science. We were able to attract a mix of European, North American, and South American participants. Out of 39 participants, 10 were women.

Specific goals of the seminar were to:

  • assess the state of data analysis in terms of fairness, transparency and diversity;
  • identify new research challenges;
  • develop an agenda for computer science research in responsible data analysis and use, with a particular focus on potential high-impact contributions from the data management community;
  • solicit perspectives on the necessary education efforts, and on responsible research and innovation practices.

The seminar included technical talks and break-out sessions. Technical talks were organized into themes, which included fairness and diversity, transparency and accountability, tracking and transparency, personal information management, education, and responsible research and innovation. Participants suggested topics for seven working groups, which met over one or multiple days.

The organizers felt that the seminar was very successful - ideas were exchanged, discussions were lively and insightful, and we are aware of several collaborations that were started as a result of the seminar. The participants and the organizers all felt that the topic of the seminar is broad, fast moving and extremely important, and that it would be beneficial to hold another seminar on this topic in the near future.

Details about the program are contained in the remainder of this document.


  1. Kate Crawford. Artificial Intelligence’s White Guy Problem. The New York Times, June 25, 2016.
  2. Kate Crawford and Ryan Calo. There is a blind spot in AI research. Nature / Comment 538(7625), October 13, 2016.
  3. Julia Angwin, Jeff Larson, Surya Mattu, and Lauren Kirchner. Machine Bias. ProPublica, May 23, 2016.
  4. Executive Office of the President, The White House. Big Data: Seizing Opportunities, Preserving Values. May 2014
  5. Parliament and Council of the European Union. General Data Protection Regulation. 2016
Summary text license
  Creative Commons BY 3.0 Unported license
  Serge Abiteboul, Gerome Miklau, Julia Stoyanovich, and Gerhard Weikum


  • Data Bases / Information Retrieval
  • World Wide Web / Internet


  • Data management
  • Data mining
  • Machine learning
  • Data analysis
  • Big data
  • Data fairness
  • Data transparency
  • Data provenance


In der Reihe Dagstuhl Reports werden alle Dagstuhl-Seminare und Dagstuhl-Perspektiven-Workshops dokumentiert. Die Organisatoren stellen zusammen mit dem Collector des Seminars einen Bericht zusammen, der die Beiträge der Autoren zusammenfasst und um eine Zusammenfassung ergänzt.


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