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Ethical Considerations in Data-Driven Fake News Detection

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Data Science for Fake News

Part of the book series: The Information Retrieval Series ((INRE,volume 42))

Abstract

Data-driven and AI-based detection of fake news has seen much recent interest. The focus of research on data-driven fake news detection has been on developing novel and effective machine learning pipelines. The field has flourished with the rapid advances in deep learning methodologies and the availability of several labelled datasets to benchmark methods. While treating fake news detection as yet another data analytics problem, there has been little work on analyzing the ethical and normative considerations within such a task. This work, in a first-of-its-kind effort, analyzes ethical and normative considerations in using data-driven automation for fake news detection. We first consider the ethical dimensions of importance within the task context, followed by a detailed discussion on adhering to fairness and democratic values while combating fake news through data-driven AI-based automation. Throughout this chapter, we place emphasis on acknowledging the nuances of the digital media domain and also attempt to outline technologically grounded recommendations on how fake news detection algorithms could evolve while preserving and deepening democratic values within society.

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P, D. (2021). Ethical Considerations in Data-Driven Fake News Detection. In: Data Science for Fake News. The Information Retrieval Series, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-030-62696-9_10

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  • DOI: https://doi.org/10.1007/978-3-030-62696-9_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62695-2

  • Online ISBN: 978-3-030-62696-9

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