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International and Interdisciplinary Conference on Modeling and Using Context

CONTEXT 2017: Modeling and Using Context pp 127–136Cite as

Accountability Breeds Response-Ability: Contextual Debiasing and Accountability in Argumentation

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10257))

Abstract

While there is growing consensus over the need to counteract biases in contexts of argumentation and decision-making, researchers disagree over which debiasing techniques are likely to be most effective. I attempt to show that contextual debiasing is more effective than cognitive debiasing in preventing biases, although I challenge the claim that critical thinking is utterly ineffective. In addition, a distinction is introduced between two types of contextual debiasing: situational correction, and dispositional correction. Drawing on empirical work on accountability, I argue that the later type of correction is more likely to prove effective against biases in everyday contexts. Holding arguers accountable is a contextual constraint that has the virtue of also enhancing cognitive skills and virtues.

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Acknowledgments

I would like to thank the editor and three anonymous reviewers for their constructive comments. Work on this article was conducted under the grant SFRH/BPD/101744/2014 by the ‘‘Portuguese Foundation for Science and Technology’’ (FCT).

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Authors and Affiliations

  1. ArgLab, Nova Institute of Philosophy (IFILNOVA), FCSH, Universidade Nova de Lisboa, Lisbon, Portugal

    Vasco Correia

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  1. Vasco Correia
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Correspondence to Vasco Correia .

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Editors and Affiliations

  1. Pierre and Marie Curie University , Paris, France

    Patrick Brézillon

  2. University of Maine , Orono, Maine, USA

    Roy Turner

  3. University of Genoa , Genoa, Italy

    Carlo Penco

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Correia, V. (2017). Accountability Breeds Response-Ability: Contextual Debiasing and Accountability in Argumentation. In: Brézillon, P., Turner, R., Penco, C. (eds) Modeling and Using Context. CONTEXT 2017. Lecture Notes in Computer Science(), vol 10257. Springer, Cham. https://doi.org/10.1007/978-3-319-57837-8_10

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  • DOI: https://doi.org/10.1007/978-3-319-57837-8_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57836-1

  • Online ISBN: 978-3-319-57837-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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