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The ethics of scientific recommender systems

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  • Andrea Polonioli

    (Coveo Solutions Inc)

Abstract

Scientific recommender systems have become increasingly popular as a tool to overcome information overload, allowing researchers to access fresh and relevant content. However, this article presents an analysis of the most pressing ethical challenges posed by recommender systems in the context of scientific research. In particular, it is argued that scientific recommender systems may risk isolating scholars in information bubbles and insulating them from exposure to different viewpoints. Further, they also risk suffering from popularity biases which may lead to a winner-takes-all scenario and reinforce discrepancies in recognition received by eminent scientists and unknown researchers. The article concludes with recommendations for scientists, journals, and digital libraries to facilitate progress in the study of the ethics of scientific recommender systems.

Suggested Citation

  • Andrea Polonioli, 2021. "The ethics of scientific recommender systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1841-1848, February.
  • Handle: RePEc:spr:scient:v:126:y:2021:i:2:d:10.1007_s11192-020-03766-1
    DOI: 10.1007/s11192-020-03766-1
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    References listed on IDEAS

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    2. Andrea Polonioli, 2020. "In search of better science: on the epistemic costs of systematic reviews and the need for a pluralistic stance to literature search," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1267-1274, February.
    3. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    4. Elizabeth Gibney, 2014. "How to tame the flood of literature," Nature, Nature, vol. 513(7516), pages 129-130, September.
    5. Fabrizio Germano & Vicenç Gómez & Gaël Le Mens, 2019. "The few-get-richer: a surprising consequence of popularity-based rankings," Economics Working Papers 1636, Department of Economics and Business, Universitat Pompeu Fabra.
    6. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
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