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Artificial intelligence in the information ecosystem: Affordances for everyday information seeking

Hirvonen, Noora, Jylhä, Ville, Lao, Yucong, Larsson, Stefan·2024·Journal of the Association for Information Science and Technology
DOI 10.1002/asi.24860
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Abstract

In this conceptual article, we argue that artificial intelligence (AI) systems are contributing to the generation of an environment of affordances for everyday information practices through which they exert influence on people and the planet in ways that often are left unrecognized. We illustrate our insights by focusing on the practices of information seeking in everyday life, suggesting that the affordances of AI systems integrated into search engines, social media platforms, streaming services, and media generation, shape such practices in ways that may, paradoxically, result both in the increase and reduction of diversity of and access to information. We discuss the potential implications of these developments in terms of the sustainability of information ecosystems and suggest solutions for addressing them through regulation and education. Drawing from the fields of library and information science and science and technology studies and research on affordances, everyday information practices, and sustainability, the article seeks to respond to the need for more nuanced theoretical insights on the impact and implications of AI on information practices and to develop conceptual tools with which to examine the co‐evolution of humans and information systems from a systemic perspective.

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  • Recent work suggests that AI systems can reshape information-seeking practices by influencing how users search, retrieve, scan, and evaluate information, while also potentially discouraging the triangulation of sources.

    “This perspective becomes especially relevant in AI-mediated contexts, as recent work suggests that AI systems can reshape information-seeking practices by influencing how users search, retrieve, scan, and evaluate information, while also potentially discouraging the triangulation of sources [12].”

    From Engineering student perceptions of generative AI use in learning trust judgment and information seeking (2026) · cites this paper as [12]

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Cited by

Sorted by citations

Authors
Hirvonen, N., Jylhä, V., Lao, Y., & Larsson, S.
Affiliations
  • Information Studies University of Oulu Oulu Finland
  • Department of Technology and Society Lund University Lund Sweden
Venue
Journal of the Association for Information Science and Technology
Publisher
Wiley
Published
2023-12-29
Volume
75
Issue
10
Pages
1152-1165
ISSN
  • 2330-1635
  • 2330-1643
DOI
10.1002/asi.24860
Cited by
80
References
15
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)
Funders
Academy of Finland (Profi4 318930)

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