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Automated Journalism as a Source of and a Diagnostic Device for Bias in Reporting

Author

Listed:
  • Leo Leppänen

    (Department of Computer Science, University of Helsinki, Finland)

  • Hanna Tuulonen

    (Swedish School of Social Science, University of Helsinki, Finland)

  • Stefanie Sirén-Heikel

    (Media and Communication Studies, University of Helsinki, Finland)

Abstract

In this article we consider automated journalism from the perspective of bias in news text. We describe how systems for automated journalism could be biased in terms of both the information content and the lexical choices in the text, and what mechanisms allow human biases to affect automated journalism even if the data the system operates on is considered neutral. Hence, we sketch out three distinct scenarios differentiated by the technical transparency of the systems and the level of cooperation of the system operator, affecting the choice of methods for investigating bias. We identify methods for diagnostics in each of the scenarios and note that one of the scenarios is largely identical to investigating bias in non-automatically produced texts. As a solution to this last scenario, we suggest the construction of a simple news generation system, which could enable a type of analysis-by-proxy. Instead of analyzing the system, to which the access is limited, one would generate an approximation of the system which can be accessed and analyzed freely. If successful, this method could also be applied to analysis of human-written texts. This would make automated journalism not only a target of bias diagnostics, but also a diagnostic device for identifying bias in human-written news.

Suggested Citation

  • Leo Leppänen & Hanna Tuulonen & Stefanie Sirén-Heikel, 2020. "Automated Journalism as a Source of and a Diagnostic Device for Bias in Reporting," Media and Communication, Cogitatio Press, vol. 8(3), pages 39-49.
    • Handle: RePEc:cog:meanco:v:8:y:2020:i:3:p:39-49
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    References listed on IDEAS

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    1. Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2018. "Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 22-27, May.
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    Cited by:

    1. Rodrigo Zamith & Mario Haim, 2020. "Algorithmic Actants in Practice, Theory, and Method," Media and Communication, Cogitatio Press, vol. 8(3), pages 1-4.

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