Breaking the filter bubble: democracy and design

By Engin Bozdag, Jeroen van den Hoven. Ethics and Information Technology, 2015

337 citations recorded by the source corpus.

It has been argued that the Internet and social media increase the number of available viewpoints, perspectives, ideas and opinions available, leading to a very diverse pool of information. However, critics have argued that algorithms used by search engines, social networking platforms and other large online intermediaries actually decrease information diversity by forming so-called “filter bubbles”. This may form a serious threat to our democracies. In response to this threat others have developed algorithms and digital tools to combat filter bubbles. This paper first provides examples of different software designs that try to break filter bubbles. Secondly, we show how norms required by two democracy models dominate the tools that are developed to fight the filter bubbles, while norms of other models are completely missing in the tools. The paper in conclusion argues that democracy itself is a contested concept and points to a variety of norms. Designers of diversity enhancing tools must thus be exposed to diverse conceptions of democracy.

Subfields: Communication, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/d1a40d8b27788faba234a0680b4a02fe5b7c00f6

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Citations in the corpus

Explaining Credibility in News Articles using Cross-Referencing (2019, background): “, users can make better decisions only when exposed to diverse viewpoints [2].”
Explanations as Mechanisms for Supporting Algorithmic Transparency (2018, background): “In a social media context, algorithmic curation—automated selection and ranking of content— acts as a gatekeeper, defining what is relevant, knowable, and authoritative [27, 6, 23].”
A Multidisciplinary Survey and Framework for Design and Evaluation of Explainable AI Systems (2018, background): “For instance, recently, news feed and targeted advertising algorithms in social media have attracted much attention for aggravating the lack of information diversity in social media [23].”
Recommender systems and their ethical challenges (2020, background): “In this respect, Bozdag and van den Hoven (2015) argue that the design of algorithmic tools to combat informational segregation should be more sensitive to the democratic norms that are implicitly built into these tools.”
Seven HCI Grand Challenges (2019, background): “For instance, it has been argued that the Internet and social media would increase the availability of perspectives, ideas, and opinions, yet in reality information is eventually “ filtered ” by algorithms that actually decrease information diversity (Bozdag & van Den Hoven, 2015).”
Towards Algorithmic Experience: Initial Efforts for Social Media Contexts (2018, background): “The algorithmic effects on social media Social media have increasingly come into focus as having societal impact, such as in the discussion of their role in creating “filter bubbles” and “echo chambers” [1,3,4,6,19,20,24].”
Algorithmic amplification of politics on Twitter (2021, background): “As of 5 June 2020, the experiment included 58 million unique Twitter user IDs (58,087,969, 5% of all accounts globally), of which 20% (11,617,373) are assigned to control, and 80% (46,470,596) are assigned to the treatment group.”