As people increasingly rely on online media and recommender systems to consume information, engage in debates, and form their political opinions, the design goals of online media and news recommenders have wide implications for the political and social processes that take place online and offline. Current recommender systems have been observed to promote personalization and more effective forms of informing, but also to narrow the user’s exposure to diverse content. Concerns about echo-chambers and filter bubbles highlight the importance of design metrics that can successfully strike a balance between fairness and accurate recommendations that respond to individual information needs and preferences. This balance is additionally complicated by concerns about missing out important information, context and the broader cultural and political diversity in the news.

The goal of this Dagstuhl Perspectives Workshop is to develop a broader, more sophisticated vision of the future of personalized recommenders – a vision that should be developed as the result of a collaborative effort by different areas of academic research (media studies, computer science, law and legal philosophy, communication science, political philosophy, and democratic theory). Collaborating across disciplines, the workshop aims at developing a new perspective on diversity and fairness in recommender systems to address the societal and technical challenges current systems raise. This perspective shall both be rooted in social, political, and computer science theory as well as point in concrete directions, where solutions and opportunities in addressing these challenges may lie.

The workshop will, hence, produce:

• A much-needed vision on the role of AI and data analytics for the democratic role of the media.
• Best practices and insights into interdisciplinary engagement in value-sensitive algorithmic design of recommendation algorithms
• Guidelines as well as a manifesto for future research and long-term goals for the emerging topics of fairness, diversity, and personalization in recommender systems.

Creative Commons BY 3.0 DE

Abraham Bernstein, Claes De Vreese, Natali Helberger, Wolfgang Schulz, and Katharina A. Zweig

• Christian Baden (The Hebrew University of Jerusalem, IL) [dblp]
• Michael Beam (Kent State University, US) [dblp]
• Abraham Bernstein (Universität Zürich, CH) [dblp]
• Claes De Vreese (University of Amsterdam, NL) [dblp]
• Marc Hauer (TU Kaiserslautern, DE)
• Lucien Heitz (Universität Zürich, CH) [dblp]
• Natali Helberger (University of Amsterdam, NL) [dblp]
• Pascal Jürgens (Johannes Gutenberg-Universität Mainz, DE) [dblp]
• Christian Katzenbach (Institute for Internet & Society - Berlin, DE) [dblp]
• Benjamin Kille (TU Berlin, DE) [dblp]
• Beate Klimkiewicz (University Jagiellonski - Krakow, PL) [dblp]
• Wiebke Loosen (Universität Hamburg, DE) [dblp]
• Judith Möller (University of Amsterdam, NL)
• Goran Radanovic (MPI-SWS - Saarbrücken, DE) [dblp]
• Wolfgang Schulz (Universität Hamburg, DE) [dblp]
• Guy Shani (Ben Gurion University - Beer Sheva, IL) [dblp]
• Nava Tintarev (TU Delft, NL) [dblp]
• Suzanne Tolmeijer (Universität Zürich, CH) [dblp]
• Wouter van Atteveldt (VU University Amsterdam, NL)
• Sanne Vrijenhoek (VU University Amsterdam, NL) [dblp]
• Theresa Züger (Institute for Internet & Society - Berlin, DE)
• Katharina A. Zweig (TU Kaiserslautern, DE) [dblp]