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Cited by 100Human-Automation Interaction and SafetyExplainable Artificial Intelligence (XAI)Impact of AI and Big Data on Business and Society

The risk of algorithm transparency: How algorithm complexity drives the effects on the use of advice

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Lehmann, Cedric A., Haubitz, Christiane B., Fügener, Andreas, Thonemann, Ulrich W.·2022·Production and Operations Management
DOI 10.1111/poms.13770
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Abstract

Although algorithmic decision support is omnipresent in many managerial tasks, a lack of algorithm transparency is often stated as a barrier to successful human–machine collaboration. In this paper, we analyze the effects of algorithm transparency on the use of advice from algorithms with different degrees of complexity. We conduct a set of laboratory experiments in which participants receive identical advice from algorithms with different levels of transparency and complexity. Our results indicate that not the algorithm itself, but the individually perceived appropriateness of algorithmic complexity moderates the effects of transparency on the use of advice. We summarize this effect as a plateau curve: While perceiving an algorithm as too simple severely harms the use of its advice, the perception of an algorithm as being too complex has no significant effect. Our insights suggest that managers do not have to be concerned about revealing algorithms that are perceived to be appropriately complex or too complex to decision‐makers, even if the decision‐makers do not fully comprehend them. However, providing transparency on algorithms that are perceived to be simpler than appropriate could disappoint people's expectations and thereby reduce the use of their advice.

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  • Users tend to associate larger models and longer results with greater precision and reliability.

    “Previous studies also show that users tend to associate larger models and longer results with greater precision and reliability [25], [26], [27]”

    From Hollow-LLM Attack: Computationally Trivial Weights in Zero-Knowledge Verification of LLM Inference (2026) · cites this paper as [26]

    Section 4.3.2 reports that making a complex algorithm transparent increased participants’ weight on its advice, which lends some support to the claim about users’ responses to more complex models. The paper does not establish that users viewed larger models as more precise or reliable, and it does not examine the length of results. Section 5.3.3 also finds no additional increase in reliance when an algorithm was perceived as more complex than appropriate. (LLM, conf=0.78)

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Cited by

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Authors
Lehmann, C. A., Haubitz, C. B., Fügener, A., & Thonemann, U. W.
Affiliations
University of Cologne (DE)
Corresponding author
Thonemann, Ulrich W.
ORCID
Venue
Production and Operations Management
Publisher
SAGE Publications
Published
2022-09
Volume
31
Issue
9
Pages
3419-3434
ISSN
  • 1059-1478 (ISSN-L)
  • 1937-5956
DOI
10.1111/poms.13770
OpenAlex
W3189444386
Cited by
100
References
9
Citations by year
2026: 33 · 2025: 37 · 2024: 19 · 2023: 7 · 2022: 3 · 2020: 1
FWCI
15.59
Type
journal article
Language
en
Open access
Licence
Locations
  • Production and Operations Management (publishedVersion, cc-by-nc-nd)
  • Kölner Universitäts PublikationsServer (Universität zu Köln) (publishedVersion)
  • Kölner Universitäts PublikationsServer (Universität zu Köln) (acceptedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Econstor (Econstor) (submittedVersion)
  • Econstor (Econstor) (submittedVersion, cc-by-nc-nd)
Funders
Deutsche Forschungsgemeinschaft (EXC 2126/1 ‐ 390838866)
Topics
  • Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
Keywords
algorithmic transparency, algorithmic decision support, algorithmic complexity, human-machine collaboration, perceived appropriateness, management decision-making, trust in algorithms

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