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Cited by 145Ethics and Social Impacts of AIRisk Perception and ManagementScience Education and Perceptions

The DOI matches its Crossref record; 73 of the 74 references it cites were found in a registry or an index.

What does the public think about artificial intelligence?—A criticality map to understand bias in the public perception of AI

Open access
Brauner, Philipp, Hick, Alexander, Philipsen, Ralf, Ziefle, Martina·2023·Frontiers in Computer Science
DOI 10.3389/fcomp.2023.1113903
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Abstract

Introduction: Artificial Intelligence (AI) has become ubiquitous in medicine, business, manufacturing and transportation, and is entering our personal lives. Public perceptions of AI are often shaped either by admiration for its benefits and possibilities, or by uncertainties, potential threats and fears about this opaque and perceived as mysterious technology. Understanding the public perception of AI, as well as its requirements and attributions, is essential for responsible research and innovation and enables aligning the development and governance of future AI systems with individual and societal needs. Methods: To contribute to this understanding, we asked 122 participants in Germany how they perceived 38 statements about artificial intelligence in different contexts (personal, economic, industrial, social, cultural, health). We assessed their personal evaluation and the perceived likelihood of these aspects becoming reality. Results: We visualized the responses in a criticality map that allows the identification of issues that require particular attention from research and policy-making. The results show that the perceived evaluation and the perceived expectations differ considerably between the domains. The aspect perceived as most critical is the fear of cybersecurity threats, which is seen as highly likely and least liked. Discussion: The diversity of users influenced the evaluation: People with lower trust rated the impact of AI as more positive but less likely. Compared to people with higher trust, they consider certain features and consequences of AI to be more desirable, but they think the impact of AI will be smaller. We conclude that AI is still a “black box” for many. Neither the opportunities nor the risks can yet be adequately assessed, which can lead to biased and irrational control beliefs in the public perception of AI. The article concludes with guidelines for promoting AI literacy to facilitate informed decision-making.

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Research works printed but not linked: 8

  1. Language models are few-shot learners,18771901 BrownT. MannB. RyderN. SubbiahM. KaplanJ. D. DhariwalP. LarochelleH. RanzatoM. HadsellR. BalcanM. F. LinH. 35785085Curran Associates, Inc.Advances in neural information processing systems2020
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  5. Using matchboxes to teach the basics of machine learning: An analysis of (possible) misconceptions,2529 MarxE. LeonhardtT. BaberowskiD. BergnerN. KinnairdK. M. SteinbachP. GuhrO. Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop Proceedings of Machine Learning Research2022
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  7. Russell, 2009, Artificial Intelligence: A Modern Approach, 3rd Edn
  8. Wilkowska, 2018, Rethinking technology development for older adults: a responsible research and innovation duty,, Aging, Technology and Health, 1

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Metadata

Authors
Brauner, P., Hick, A., Philipsen, R., & Ziefle, M.
Affiliations
RWTH Aachen University (DE)
Corresponding author
Brauner, Philipp
ORCID
Venue
Frontiers in Computer Science
Publisher
Frontiers Media SA
Published
2023-03-16
Volume
5
ISSN
2624-9898 (ISSN-L)
DOI
10.3389/fcomp.2023.1113903
OpenAlex
W4327620114
Cited by
143
References
74
Citations by year
2026: 42 · 2025: 56 · 2024: 39 · 2023: 6
FWCI
35.78
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Computer Science (publishedVersion, cc-by)
  • RWTH Publications (RWTH Aachen) (publishedVersion, other-oa)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Social Science Open Access Repository (GESIS – Leibniz Institute for the Social Sciences) (submittedVersion)
  • OSF Preprints (OSF Preprints) (submittedVersion)
Funders
  • Deutsche Forschungsgemeinschaft
  • Horizon 2020 Framework Programme
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Sociology and Political Science › Risk Perception and Management
  • Social Sciences › Psychology › Developmental and Educational Psychology › Science Education and Perceptions
Keywords
public perception of AI, perceived bias, trust in AI, AI literacy, cyber threats, control beliefs, responsible research and innovation, AI governance

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