The perils and pitfalls of explainable AI: Strategies for explaining algorithmic decision-making
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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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In complex public-law settings, explanations are embedded in contested legal standards, institutional purposes and discretionary judgments.
“In complex public-law settings, explanations are embedded in contested legal standards, institutional purposes and discretionary judgments (de Bruijn et al., 2022;Oswald, 2018).”
From From Human Oversight to Cognitive Sovereignty: A Process-Based Governance Standard for AI-Assisted Legal Reasoning (2026) · cites this paper as de Bruijn et al., 2022
Section 7 (page unavailable) says government AI explanations can be contested and that the problems they address are ambiguous. Section 6.4 (page unavailable) says transparency also requires institutional rules and oversight. These passages support the claim’s broader point, but they do not specifically describe explanations as embedded in contested legal standards, institutional purposes, and discretionary judgments. (LLM, conf=0.82)
Experimental — accuracy not yet measuredDe Bruijn, Warnier and Janssen (2022) show that explainable AI is not a simple cure for public-sector algorithmic decisionmaking because explanations operate within contested legal and administrative contexts.
“De Bruijn, Warnier and Janssen (2022) show that explainable AI is not a simple cure for public-sector algorithmic decisionmaking because explanations operate within contested legal and administrative contexts.”
From From Human Oversight to Cognitive Sovereignty: A Process-Based Governance Standard for AI-Assisted Legal Reasoning (2026) · cites this paper as De Bruijn, Warnier and Janssen (2022)
Explainable AI, at least in its current form, may not address the informational needs of laypeople, politicians, professionals, or scientists because the information is too technical.
“Explainable AI, at least in its current form, may not address the informational needs of laypeople, politicians, professionals, or scientists because the information is too technical [58].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [58]
There is very little agreement about what explainability means in practice, because what makes AI explainable depends on the context of its use.
“there is very little agreement about what explainability means in practice, because what makes AI explainable depends on the context of its use [58].”
From The ethics of using artificial intelligence in scientific research: new guidance needed for a new tool (2025) · cites this paper as [58]
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- Authors
- de Bruijn, H., Warnier, M., & Janssen, M.
- Affiliations
- Delft University of Technology (NL)
- Corresponding author
- Janssen, Marijn
- Venue
- Government Information Quarterly
- Publisher
- Elsevier BV
- Published
- 2022-04
- Volume
- 39
- Issue
- 2
- Pages
- 101666
- ISSN
- 0740-624X (ISSN-L)
- OpenAlex
- W4205177168
- Cited by
- 264
- References
- 37
- Citations by year
- 2026: 66 · 2025: 74 · 2024: 63 · 2023: 44 · 2022: 15 · 2021: 1 · 2017: 1
- FWCI
- 10.87
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- 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)
- Social Sciences › Safety Research › 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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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