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Cited by 264Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AIE-Government and Public Services

The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.

The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making

Open access
de Bruijn, Hans, Warnier, Martijn, Janssen, Marijn·2022·Government Information Quarterly
DOI 10.1016/j.giq.2021.101666
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Abstract

Governments look at explainable artificial intelligence's (XAI) potential to tackle the criticisms of the opaqueness of algorithmic decision-making with AI. Although XAI is appealing as a solution for automated decisions, the wicked nature of the challenges governments face complicates the use of XAI. Wickedness means that the facts that define a problem are ambiguous and that there is no consensus on the normative criteria for solving this problem. In such a situation, the use of algorithms can result in distrust. Whereas there is much research advancing XAI technology, the focus of this paper is on strategies for explainability. Three illustrative cases are used to show that explainable, data-driven decisions are often not perceived as objective by the public. The context might raise strong incentives to contest and distrust the explanation of AI, and as a consequence, fierce resistance from society is encountered. To overcome the inherent problems of XAI, decisions-specific strategies are proposed to lead to societal acceptance of AI-based decisions. We suggest strategies to embrace explainable decisions and processes, co-create decisions with societal actors, move away from an instrumental to an institutional approach, use competing and value-sensitive algorithms, and mobilize the tacit knowledge of professionals

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Research works printed but not linked: 2

  1. LeCun, 2015, Deep learning., 521, 436
  2. McDonald, 2019, AI system for granting UK visas is biased, rights groups claim, The Gaurdian, 29

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Metadata

Authors
de Bruijn, H., Warnier, M., & Janssen, M.
Affiliations
Delft University of Technology (NL)
Corresponding author
Janssen, Marijn
ORCID
Venue
Government Information Quarterly
Publisher
Elsevier BV
Published
2022-04
Volume
39
Issue
2
Pages
101666
ISSN
0740-624X (ISSN-L)
DOI
10.1016/j.giq.2021.101666
OpenAlex
W4205177168
Cited by
264
References
37
Citations by year
2026: 66 · 2025: 74 · 2024: 63 · 2023: 44 · 2022: 15
FWCI
10.87
Type
journal article
Language
en
Open access
Licence
Locations
  • Government Information Quarterly (publishedVersion, cc-by)
  • Lirias (KU Leuven) (submittedVersion, cc-by)
  • Research Repository (Delft University of Technology) (submittedVersion, cc-by)
  • Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
  • Social Sciences › Political Science and International Relations › E-Government and Public Services
Keywords
explainable AI, algorithmic decision-making, algorithmic transparency, public trust in AI, wicked problems, automated decision-making, government AI policy

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