Bias in algorithmic filtering and personalization


Online information intermediaries such as Facebook and Google are slowly replacing traditional media channels thereby partly becoming the gatekeepers of our society. To deal with the growing amount of information on the social web and the burden it brings on the average user, these gatekeepers recently started to introduce personalization features, algorithms that filter information per individual. In this paper we show that these online services that filter information are not merely algorithms. Humans not only affect the design of the algorithms, but they also can manually influence the filtering process even when the algorithm is operational. We further analyze filtering processes in detail, show how personalization connects to other filtering techniques, and show that both human and technical biases are present in today’s emergent gatekeepers. We use the existing literature on gatekeeping and search engine bias and provide a model of algorithmic gatekeeping.

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  1. 1.

    For instance, Facebook uses an algorithm called Edgerank to determine how a newsfeed of a user is constructed. It is believed that several factors are used to select/prioritize user updates, such as affinity between the receiver and sender, and the date of the published update. However, the exact formula is unknown. See Techcrunch (2011).

  2. 2.

    We would like to thank the anonymous reviewers to point out this fact.


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The author would like to thank Martijn Warnier and Ibo van de Poel for their valuable comments. This research is supported by the Netherlands Organization for Scientific Research (NWO) Mozaiek grant, file number 017.007.111.

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Correspondence to Engin Bozdag.

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Bozdag, E. Bias in algorithmic filtering and personalization. Ethics Inf Technol 15, 209–227 (2013).

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  • Information politics
  • Bias
  • Social filtering
  • Algorithmic gatekeeping