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The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks

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Artificial Intelligence in HCI (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14050))

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Abstract

Artificial intelligence (AI) outperforms humans in plentiful domains. Despite security and ethical concerns, AI is expected to provide crucial improvements on both personal and societal levels. However, algorithm aversion is known to reduce the effectiveness of human-AI interaction and diminish the potential benefits of AI. In this paper, we built upon the Dual System Theory and investigate the effect of the AI response time on algorithm aversion for slow-thinking and fast-thinking tasks. To answer our research question, we conducted a 2\(\,\times \,\)2 incentivized laboratory experiment with 116 students in an advice-taking setting. We manipulated the length of the AI response time (short vs. long) and the task type (fast-thinking vs. slow-thinking). Additional to these treatments, we varied the domain of the task. Our results demonstrate that long response times are associated with lower algorithm aversion, both when subjects think fast and slow. Moreover, when subjects were thinking fast, we found significant differences in algorithm aversion between the task domains.

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Acknowledgements

This research is funded by the German Federal Ministry of Education and Research (BMBF) within the “The Future of Value Creation - Research on Production, Services and Work” program (02L19C115). Olesja Lammert and Jaroslaw Kornowicz acknowledge funding by the Deutsche Forschungsgemeinschaft (TRR 318/1 2021 - 438445824). The authors are responsible for the content of this publication. The authors thank Kirsten Thommes and René Fahr for valuable discussion and constructive comments.

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Authors and Affiliations

  1. Paderborn University, Warburgerstr. 100, 33098, Paderborn, Germany

    Anastasia Lebedeva, Jaroslaw Kornowicz, Olesja Lammert & Jörg Papenkordt

Authors
  1. Anastasia Lebedeva
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  2. Jaroslaw Kornowicz
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  3. Olesja Lammert
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  4. Jörg Papenkordt
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Correspondence to Anastasia Lebedeva .

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Editors and Affiliations

  1. Siemens Corporation, Princeton, NJ, USA

    Helmut Degen

  2. Foundation for Research and Technology – Hellas (FORTH), Heraklion, Crete, Greece

    Stavroula Ntoa

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Lebedeva, A., Kornowicz, J., Lammert, O., Papenkordt, J. (2023). The Role of Response Time for Algorithm Aversion in Fast and Slow Thinking Tasks. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2023. Lecture Notes in Computer Science(), vol 14050. Springer, Cham. https://doi.org/10.1007/978-3-031-35891-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-35891-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-35890-6

  • Online ISBN: 978-3-031-35891-3

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