Meaningful XAI Based on User-centric Design Methodology: Combining Legal and Human-computer Interaction (HCI) Approaches to Achieve Meaningful Algorithmic Explainability
Open accessAbstract
This report takes stock of XAI-related requirements appearing in various EU directives, regulations, guidelines, and CJEU case law. This analysis of existing requirements will permit us to have a clearer vision of the purposes, the “why”, of XAI, which we separate into five categories: contestability, empowerment/redressing information asymmetries, control over system performance, evaluation of algorithmic decisions, and public administration transparency. The analysis of legal requirements also permits us to create four categories of recipients for explainability: data science teams; human operators of the system; persons affected by algorithmic decisions, and regulators/judges/auditors. Lastly, we identify four main operational contexts for explainability: XAI for the upstream design and testing phase; XAI for human-on-the-loop control; XAI for human-in-the-loop control; and XAI for ex- post challenges and investigations. Second, we will present user-centered design methodology, which takes the purposes, the recipients and the operational context into account in order to develop optimal XAI solutions. Third, we will suggest a methodology to permit suppliers and users of high-risk AI applications to propose local XAI solutions that are effective in the sense of being “meaningful”, for example, useful in light of the operational, safety and fundamental rights contexts. The process used to develop these “meaningful” XAI solutions will be based on user-centric design principles examined in the second part. Fourth, we will suggest that the European Commission issue guidelines to provide a harmonised approach to defining “meaningful” explanations based on the purposes, audiences and operational contexts of AI systems. These guidelines would apply to the AI Act, but also to the other EU texts requiring explanations for algorithmic systems and results.
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Contestability is a core ethical requirement in algorithmic decision-making and a central element in several worldwide legislations.
“Contestability is a core ethical requirement in algorithmic decision-making and, consequently, a central element in several worldwide legislations (Vaccaro et al. 2019;Maxwell and Dumas 2023).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Maxwell and Dumas 2023
Section 3 (p. 19) identifies contestability as a purpose of legal explainability requirements, and §3.1 (p. 19) says the GDPR and Convention 108+ aim to enhance it. The paper also discusses French law (§3.7, p. 27), but these examples do not establish that contestability is a central element of legislation worldwide. The cited passages do not expressly characterize it as a core ethical requirement. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAlgorithmic contestability remains under-researched, especially from a technical perspective.
“It is widely agreed that algorithmic contestability remains under-researched, especially from a technical perspective (Lyons, Velloso, and Miller 2021;Alfrink et al. 2023;Maxwell and Dumas 2023;Yurrita et al. 2025).”
From Explainable AI Isn't Enough! Rethinking Algorithmic Contestability (2026) · cites this paper as Maxwell and Dumas 2023
Section 4.4 (p. 35) says AI explainability techniques are still in their infancy and general guidelines have yet to emerge. Section 2.4 (p. 17) identifies explanations used to challenge decisions as a form of contestability, but the paper does not say that contestability itself is under-researched, particularly from a technical perspective. (LLM, conf=0.78)
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Cites
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Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
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The Impact of Placebic Explanations on Trust in Intelligent Systems
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A framework to contest and justify algorithmic decisions
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Explainable AI Isn't Enough! Rethinking Algorithmic Contestability
Freiesleben, T., Meding, K., & König, G. (2026) · arXiv · doi:10.48550/arxiv.2605.16041
- Authors
- Maxwell, W., & Dumas, B.
- Affiliations
- Télécom Paris (FR)
- University of Namur (BE)
- Corresponding author
- Maxwell, Winston
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2023
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W4387587746
- Cited by
- 11
- References
- 4
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 6
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Social Sciences › Law › Digitalization, Law, and Regulation
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- explainable AI, user-centered design, algorithmic explainability, EU AI regulation, EU AI Act, high-risk AI systems, human-in-the-loop control, information asymmetry, algorithmic transparency, fundamental rights, European Court of Justice case law
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