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REVIEW 3 major objections 5 minor 45 references

Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap

T0 review · 3 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read EU law grants a real Right to Explanation, but a systematic review of the post-2024 literature finds only 19 papers that substantively integrate law and XAI, and the field misstates the legal basis, ignores the key court ruling, and…

desk verdict A genuinely useful law-XAI systematic review with a solid conceptual core; the 19/57 count is credible but rests on a screening pipeline that is thinner than the conclusions imply. read the letter →

arxiv 2608.02699 v1 pith:S52HHXNY submitted 2026-08-03 cs.AI cs.CYcs.LG

Benjamin Fresz , Elena Dubovitskaya , Marco F. Huber This is my paper · ORCID
classification cs.AIcs.CYcs.LG
keywords RighttoExplanationGDPRArticle15(1)(h)AIAct86ExplainablesystematicliteraturereviewAddressee/PurposeFrameworkcounterfactualexplanationsDun&Bradstreetjudgment
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that the EU Right to Explanation is a real, enforceable legal entitlement that current explainable-AI research cannot yet satisfy. Reviewing 2,643 records and reading 57 full texts, it finds only 19 papers that substantively engage both EU law and XAI, and it documents three systematic failures across that literature: most papers ground the GDPR right in Art. 22 and Recital 71 rather than Art. 15(1)(h), almost none engage the Court of Justice's Dun & Bradstreet judgment, and most mix up the form an explanation must take with the content it must carry. The paper's answer is the Addressee/Purpose Framework: the addressee fixes the form, the legal purpose fixes the content, and both dimensions are independently required. If that is right, the practical consequence is stark: no current XAI method reliably produces a legally compliant explanation, so the Right to Explanation remains a formal obligation without a technically realizable path to compliance unless standard-setting and user research close the gap.

What carries the argument

The load-bearing object is the Addressee/Purpose Framework, defined as the claim that the addressee of an explanation determines its required form while the legal purpose of the triggering provision determines its required content. The framework maps directly onto the law: Art. 12(1) GDPR governs form, and the substantive provisions (Art. 15(1)(h) GDPR, Art. 86 AIA, Art. 18(8)(a) CCD) govern content, with the CJEU's Dun & Bradstreet judgment requiring both dimensions simultaneously and independently. It does the work of showing why a pure SHAP visualization fails both dimensions and why a plain-language narrative can still fail if it omits contrastive or counterfactual information. The companion machinery is a four-phase operationalization blueprint: identify applicable requirements, break them into quantifiable sub-requirements, evaluate XAI methods against those sub-requirements, and argue tradeoffs with explicit documentation of non-fulfillment.

What would settle it

Conduct a full dual-reviewer screen of all 2,643 records (or a much larger validation sample than the 60 used) and count how many additional papers meet every inclusion criterion; finding more than a handful would overturn the claim that only 19 papers substantively integrate EU law and XAI.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that the post-2024 literature on explainable AI and EU law is not merely incomplete but structurally misaligned with the law it claims to implement. The paper shows that the GDPR right to explanation is anchored in Art. 15(1)(h) GDPR, that the Court of Justice of the European Union settled its existence and its 'meaningful' quality standard in Dun & Bradstreet in February 2025, and that Art. 86 AIA and Art. 18(8)(a) CCD add separate, partly subsidiary rights. Against that legal baseline, most surveyed papers cite the wrong provision (Art. 22 or Recital 71), few cite the court ruling, and almost none separate the addressee-driven form requirement (Art. 12(1) GDPR: concise, transparent, intelligible, plain language) from the purpose-driven content requirement (what the explanation must contain so the person can contest the decision). The paper proposes the Addressee/Purpose Framework to make that separation explicit, plus a four-phase blueprint from identifying applicable requirements to documenting tradeoffs and non-fulfillment.

Load-bearing premise

The picture rests on the screening pipeline: the search required explicit EU legislation in title, abstract, or keywords, and a single technical reviewer judged relevance with a stopping rule reached after screening about 8 percent of the 2,643 records.

Editorial extensions

If this is right

  • A legally compliant explanation must satisfy form and content independently; if a system cannot produce an honest, intelligible explanation for the specific decision, the paper's Phase 4 conclusion is that the system must not be deployed in that context.
  • Standards bodies need to define conformance criteria for Art. 86 AIA explanations; current standardization work does not cover that provision, so verifiable thresholds for 'meaningful' and 'intelligible' do not exist.
  • Research and practice should stop grounding the GDPR right in Art. 22 and Recital 71; the correct anchor is Art. 15(1)(h) GDPR as interpreted by Dun & Bradstreet.
  • Explanation form cannot be verified by technical properties alone; user studies with the relevant non-expert population are required, and the disagreement problem must be treated as a potential violation of Art. 12(1) GDPR accuracy.
  • The CCD right under Art. 18(8)(a) becomes applicable in November 2026, which puts a near-term deadline on resolving the open questions.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Extension: the Addressee/Purpose Framework can be turned into a concrete test: generate explanations for a fixed credit decision and measure whether a non-expert can identify grounds for contestation; the pass rate would operationalize 'meaningful' in a way courts could use.
  • Extension: the paper's own position implies that black-box foundation models may be legally impermissible in high-risk Art. 86 AIA domains even when they outperform interpretable alternatives, so an audit of deployed high-risk systems' actual explanation capacity would show how much of the market is currently non-compliant.
  • Extension: the single-reviewer screening and the search requirement that EU legislation be named in title, abstract, or keywords likely undercounts work that discusses the right under other terms such as 'automated decision-making' or 'contestability'; a broader-concept search would quantify that undercount.
  • Extension: the framework's distinction between form and content could be extended beyond the three instruments examined here to other transparency duties, such as Art. 13 and Art. 14 AIA, where the same conflation likely occurs.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This paper reports a PRISMA-based systematic literature review of post-2024 research at the intersection of Explainable AI (XAI) and the EU Right to Explanation. From 2643 records identified in Web of Science and Scopus, 57 full texts were screened and 19 papers were judged to substantively integrate legal and technical perspectives. The authors find three recurring problems in this corpus: most papers ground the GDPR right in Art. 22 and Recital 71 rather than Art. 15(1)(h); few engage with the CJEU's Dun & Bradstreet judgment; and many conflate the form of an explanation (governed by its addressee) with its content (governed by its legal purpose). Based on this, the paper proposes an Addressee/Purpose Framework, a four-phase blueprint for operationalizing explanation requirements, and six open research questions.

Significance. The topic is timely and the review is useful if its empirical claims hold. The paper's strengths are its explicit search strings and inclusion criteria, a PRISMA flow diagram, dual full-text review by a legal and a technical researcher, and a candid limitations section that acknowledges single-reviewer title/abstract screening and database coverage limits. The Addressee/Purpose Framework is a concrete analytical contribution that connects Art. 12(1) GDPR to explanation form and the substantive provisions to explanation content. The paper also makes falsifiable claims (e.g., the low count of dual-domain papers, the prevalence of misgrounding) that can be checked by replicating the search. However, the central quantification depends on screening completeness and coding transparency, which are currently only weakly supported.

major comments (3)
  1. [Appendix A / Section 3] The search strings reported in Appendix A (WOS: TS=(...); Scopus: TITLE-ABS-KEY=(...)) require at least one of 'European Legislation', 'GDPR', 'General Data Protection Regulation', 'Artificial Intelligence Act', 'AI Act', or 'European Law' in the title, abstract, or keywords. They do not include 'EU', 'European Union', 'EU law', 'data protection law', or 'right to explanation'. Since inclusion requires engagement with Art. 15(1)(h) GDPR and/or Art. 86 AIA, a paper whose abstract mentions 'EU law' and 'explainability' without naming those exact instruments would be missed. Expanding the search with these terms could change the 2643→57→19 counts and the prevalence of the three patterns; the authors should rerun the search and report the number of additional records and whether any would meet the inclusion criteria.
  2. [Appendix A, 'Validation'] The validation exercise described in Appendix A draws 60 records from the unscreened portion of the corpus. With a 19/2643 eligible rate (about 0.72%), the expected number of eligible records in 60 draws is less than one, and observing zero yields a 95% upper bound on the residual eligible rate of about 5%. This sample is therefore too small to substantiate the claim that the ASReview stopping criterion (40 consecutive irrelevant records, reached after screening roughly 8% of records) did not miss a material number of eligible papers. I would ask the authors to screen a larger random sample (e.g., several hundred records) or to perform a second full screening pass, and to report the results.
  3. [Section 8 / Appendix A] The paper's central findings are the three patterns in the 19-paper corpus, but the coding along the four dimensions (XAI classification, legal basis, form, content/purpose) is not reported per paper, and the excluded-study list is not released. This makes the prevalence claims impossible to audit independently. The authors should provide a supplementary table listing all 57 assessed papers with their inclusion/exclusion decisions and, for included papers, the coding of the four dimensions; this is especially important given that the authors' own prior work is part of the corpus.
minor comments (5)
  1. [Section 4.3] The statement that the literature on the GDPR legal basis presents 'a heterogeneous and overall rather blurred picture' would be more informative with a per-paper breakdown of the legal-basis coding; the current text names only a subset of the 19 papers.
  2. [Figure 1] The PRISMA flow diagram is internally consistent, but the split between 'manually' (279) and 'automatically' (2364) screened records is explained only in Appendix A; a one-sentence clarification in Section 3 would help.
  3. [Table 2] The arrows and the '⇓' row make the form/content consequence hard to parse; consider separating the form and content outcomes into distinct columns.
  4. [Section 2] The statement that the CJEU in Dun & Bradstreet 'did not lay down detailed requirements regarding the substance of explanations' would benefit from a pinpoint citation to the judgment's paragraphs.
  5. [Section 8] The exclusion of Sovrano et al. 2025 is described as due to non-indexing and because 'it did not yield a substantial additional contribution'; the second clause is an evaluative judgment that could be clarified as a relevance decision under the inclusion criteria.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the 19/57 corpus is a screening result, the legal analysis is anchored in external CJEU case law, and self-citations are not load-bearing.

full rationale

This paper is a systematic literature review, not a derivation with fitted parameters, so the main circularity patterns (fitting then predicting, importing a uniqueness theorem, smuggling an ansatz through citations) do not apply. The central empirical claims—2643 records, 57 full texts, 19 included papers, and the three observed patterns—are outputs of a disclosed PRISMA screening pipeline whose search string, ASReview stopping rule, and inclusion criteria are documented in Appendix A and Table 1. A completeness limitation, such as the small validation sample or single-reviewer screening, affects the confidence one can place in recall, but it is not a circular step because the conclusions are not obtained by algebraic or definitional manipulation of those inputs. The legal interpretation is anchored in external authorities (Art. 12(1) GDPR, Art. 15(1)(h) GDPR, Art. 86 AIA, and the CJEU's Dun & Bradstreet judgment) rather than in the authors' prior work. Self-citations appear as ordinary sources or as corpus items—Fresz et al. (2024), Dubovitskaya (2025), and Dubovitskaya and Bosold (2024) are cited for specific legal points and are also screened as part of the literature—but none of these is the sole support for the paper's central claim. The Addressee/Purpose Framework is explicitly presented as a conceptualization of the coded literature, and Appendix A states that the coding dimensions were recorded before the framework was formulated, so the framework is not an input whose definition already contains the empirical results. No step in the claimed derivation chain reduces to its own input.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The paper has no fitted parameters. Its central claims rest on legal-doctrinal assumptions (e.g., the interpretation of Art. 15(1)(h) GDPR and the subsidiary nature of Art. 86 AIA) and on methodological assumptions of the systematic review (search-string coverage, screening reliability, stopping criterion). No invented entities are introduced; the Addressee/Purpose framework is a conceptual classification, not a postulated entity.

assumptions (3)
  • domain assumption Art. 15(1)(h) GDPR is the firm legal basis for the Right to Explanation, as settled by the CJEU's Dun & Bradstreet judgment.
    The review's central critique that many papers misidentify the legal basis depends on this interpretation; the paper supports it with the CJEU ruling but does not derive it from first principles.
  • domain assumption The search strings in Web of Science and Scopus capture all relevant post-2024 literature on XAI and the EU Right to Explanation.
    The strings require explicit mention of EU legislation terms, which could miss relevant papers using different terminology; this bears directly on the 19/57 corpus count.
  • domain assumption ASReview-assisted single-reviewer title/abstract screening with a stopping criterion of 40 consecutive irrelevant records identifies relevant studies reliably.
    The review applies a stopping rule after screening 8 percent of records and assumes the ASReview performance estimate; if unreliable, eligible papers could be missed.

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Cite this review

Pith. "Pith review of Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap." pith.science (2026). https://pith.science/paper/S52HHXNY

@misc{pith2026260802699,
  author       = {Pith},
  title        = {Pith review of: Explainable AI for the EU Right to Explanation: A Systematic Review of the Law-XAI Translation Gap},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/S52HHXNY}},
  note         = {Machine review of arXiv:2608.02699}
}
read the original abstract

When algorithms make or influence consequential decisions---about loan eligibility, hiring, or healthcare---EU law grants affected individuals a Right to Explanation. Yet whether (and how) Explainable AI (XAI) can satisfy this right in practice remains poorly understood, with direct implications for individuals' ability to contest automated decisions that affect their lives. This paper presents a systematic literature review of XAI in the context of the EU Right to Explanation, with particular focus on Art. 15(1)(h) GDPR, Art. 86 AI Act (AIA), and related instruments. We consider papers published from 2024 onwards, as the final version of the AIA was published in July 2024---with Art. 86 being added late. From 2643 initial records identified by a deliberately broad search, we review 57 full texts, of which only 19 papers demonstrate substantive integration of both legal and technical perspectives, showing gaps in the interdisciplinary synthesis of the current regulatory framework. We document three problematic patterns across the corpus: Most misidentify the GDPR legal basis; few engage with the CJEU's Dun & Bradstreet judgment (likely due to publication timing); and the distinction between explanation form (governed by addressee) and content (governed by legal purpose) is often conflated. We conceptualize this as the Addressee/Purpose Framework, propose a four-phase blueprint for operationalization, and identify six concrete open research questions. Without further progress, the Right to Explanation risks remaining a formal obligation without a technically realizable path to compliance.

Figures

Figures reproduced from arXiv: 2608.02699 by the authors.

Figure 1
Figure 1. PRISMA flow diagram of the literature search and selection process. [PITH_FULL_IMAGE:figures/full_fig_p015_1.png] view at source ↗

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Reference graph

Works this paper leans on

45 extracted references · 38 canonical work pages

  1. [1]

    Technical

    Almada, Marco , year =. Technical. doi:10.2139/ssrn.5096913 , howpublished =

    work page doi:10.2139/ssrn.5096913

  2. [2]

    Glenn , year =

    Babic, Boris and Gerke, Sara and Evgeniou, Theodoros and Cohen, I. Glenn , year =. Beware explanations from. Science , volume =

    work page

  3. [3]

    Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond , journal =

    Anna Hedstr. Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond , journal =. 2023 , volume =

    work page 2023

  4. [4]

    Datenschutz-

    B\". Datenschutz-. 2024 , title =

    work page 2024

  5. [5]

    Art.\ 22 mn.\ 15 , booktitle =

    Buchner, Benedikt , year =. Art.\ 22 mn.\ 15 , booktitle =

    work page

  6. [6]

    What Makes a Good Explanation?

    Chen, Zixi and Subhash, Varshini and Havasi, Marton and Pan, Weiwei and Doshi-Velez, Finale , year =. What Makes a Good Explanation?. 2211.05667 , archivePrefix=

    arXiv

  7. [7]

    Interpretable Machine Learning:

    Molnar, Christoph , year =. Interpretable Machine Learning:

    work page

  8. [8]

    False Sense of Security in Explainable Artificial Intelligence (

    Chung, Neo Christopher and Chung, Hongkyou and Lee, Hearim and Brocki, Lennart and Chung, Hongbeom and Dyer, George , year =. False Sense of Security in Explainable Artificial Intelligence (. 2405.03820 , archivePrefix=

    arXiv

Show all 45 references
  1. [9]

    2025 , eprint=

    Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT , author=. 2025 , eprint=

    2025

  2. [10]

    International Review of Law, Computers & Technology , pages =

    Colmenarejo, Alejandra Bringas and State, Laura and Comand\`. International Review of Law, Computers & Technology , pages =. 2025 , title =

    2025

  3. [11]

    Dubovitskaya, Elena and Bosold, Gregor , year =. Die. Zeitschrift f\"

  4. [12]

    Zwischen

    Dubovitskaya, Elena , year =. Zwischen. Zeitschrift f\"

  5. [13]

    Journal of Medical Ethics , doi =

    Dur\'. Journal of Medical Ethics , doi =. 2021 , title =

    2021

  6. [14]

    An Uninterpretable Right:

    Engelfriet, Arnoud , year =. An Uninterpretable Right:. 2025 International Joint Conference on Neural Networks (IJCNN) , pages =

    2025

  7. [15]

    Future Internet , volume =

    Feretzakis, Georgios and Vagena, Evangelia and Kalodanis, Konstantinos and Peristera, Paraskevi and Kalles, Dimitris and Anastasiou, Athanasios , year =. Future Internet , volume =

  8. [16]

    Sovrano, Francesco and Vilone, Giulia and Lognoul, Michael and Longo, Luca , year =. Legal

  9. [17]

    and Horz, Christian , year =

    Fresz, Benjamin and Dubovitskaya, Elena and Brajovic, Danilo and Huber, Marco F. and Horz, Christian , year =. How Should. Proceedings of the

  10. [18]

    2024 , title =

    Fresz, Benjamin and G\". 2024 , title =. 2408.02379 , archivePrefix=

    2024 arXiv

  11. [19]

    Legal Aspects of AI in the Biomedical Field

    Gallese, Chiara , publisher =. Legal Aspects of AI in the Biomedical Field. The Role of Interpretable Models , booktitle =. doi:https://doi.org/10.1002/9781119846567.ch15 , year =

    doi

  12. [20]

    IT Professional , volume =

    G\'. IT Professional , volume =. 2025 , title =

    2025

  13. [21]

    Towards an

    Grabowicz, Przemyslaw and Byrne, Adrian and Cousins, Cyrus and Perello, Nicholas and Zick, Yair , year =. Towards an. 2307.13658v2 , archivePrefix=

  14. [22]

    A Survey of Methods for Explaining Black Box Models , journal =

    Guidotti, Riccardo and Monreale, Anna and Ruggieri, Salvatore and Turini, Franco and Giannotti, Fosca and Pedreschi, Dino , year =. A Survey of Methods for Explaining Black Box Models , journal =

  15. [23]

    Journal of AI Law and Regulation , volume =

    H\". Journal of AI Law and Regulation , volume =. 2025 , title =

    2025

  16. [24]

    and Oppenheim, Paul , year =

    Hempel, Carl G. and Oppenheim, Paul , year =. Studies in the Logic of Explanation , journal =

  17. [25]

    The Right to an Explanation Under the GDPR and the AI Act

    Juliussen, Bj rn Aslak. The Right to an Explanation Under the GDPR and the AI Act. MultiMedia Modeling. 2025

    2025

  18. [26]

    Responsibility Attribution for AI-Mediated Damages with Mechanistic Interpretability

    K \"a stner, Lena and Cordes, Johann and Zech, Herbert. Responsibility Attribution for AI-Mediated Damages with Mechanistic Interpretability. Bridging the Gap Between AI and Reality. 2026

    2026

  19. [27]

    Digital Humanism , editor =

    Kol\'. Digital Humanism , editor =. 2026 , title =

    2026

  20. [28]

    The Disagreement Problem in Explainable Machine Learning:

    Krishna, Satyapriya and Han, Tessa and Gu, Alex and Pombra, Javin and Jabbari, Shahin and Wu, Steven and Lakkaraju, Himabindu , year =. The Disagreement Problem in Explainable Machine Learning:. 2202.01602 , archivePrefix=

    arXiv

  21. [29]

    Information Fusion , volume =

    Longo, Luca and Brcic, Mario and Cabitza, Federico and Choi, Jaesik and Confalonieri, Roberto and. Information Fusion , volume =. 2024 , title =

    2024

  22. [30]

    Technology and Regulation , volume =

    Metiko. Technology and Regulation , volume =. 2024 , title =

    2024

  23. [31]

    Law, Innovation and Technology , volume =

    Metiko. Law, Innovation and Technology , volume =. 2025 , title =

    2025

  24. [32]

    Explanation in Artificial Intelligence:

    Miller, Tim , year =. Explanation in Artificial Intelligence:. 1706.07269 , archivePrefix=

    arXiv

  25. [33]

    Towards Transparent AI: How will the AI Act Shape the Future?

    Moreira, N \'i dia Andrade and Freitas, Pedro Miguel and Novais, Paulo. Towards Transparent AI: How will the AI Act Shape the Future?. Progress in Artificial Intelligence. 2025

    2025

  26. [34]

    2022 , title =

    Nauta, Meike and Trienes, Jan and Pathak, Shreyasi and Nguyen, Elisa and Peters, Michelle and Schmitt, Yasmin and Schl\". 2022 , title =. 2201.08164 , archivePrefix=

    2022 arXiv

  27. [35]

    and McKenzie, Joanne E

    Page, Matthew J. and McKenzie, Joanne E. and Bossuyt, Patrick M. and Boutron, Isabelle and Hoffmann, Tammy C. and Mulrow, Cynthia D. and Shamseer, Larissa and Tetzlaff, Jennifer M. and Akl, Elie A. and Brennan, Sue E. and Chou, Roger and Glanville, Julie and Grimshaw, Jeremy M...

    2021

  28. [36]

    , year =

    Palazzo, D. , year =. The Right to Explanation of Automated Decisions under the. European Data Protection Law Review , volume =

  29. [37]

    Unlocking the black box: analysing the

    Pavlidis, Georgios , year =. Unlocking the black box: analysing the. Law, Innovation and Technology , volume =

  30. [38]

    Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , journal =

    Rudin, Cynthia , year =. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , journal =

  31. [39]

    Algorithmic Knowability: A Unified Approach to Explanations in the AI Act

    Sapienza, Salvatore and Palmirani, Monica. Algorithmic Knowability: A Unified Approach to Explanations in the AI Act. Explainable Artificial Intelligence. 2026

    2026

  32. [40]

    2025 , title =

    Global Privacy Law Review , volume =. 2025 , title =

    2025

  33. [41]

    The explanation dialogues: an expert focus study to understand requirements towards explanations within the

    State, Laura and Colmenarejo, Alejandra Bringas and Beretta, Andrea and Ruggieri, Salvatore and Turini, Franco and Law, Stephanie , year =. The explanation dialogues: an expert focus study to understand requirements towards explanations within the. Artificial Intelligence and ...

  34. [42]

    Sanity Checks for Saliency Metrics , eprint=

    Tomsett, Richard and Harborne, Dan and Chakraborty, Supriyo and Gurram, Prudhvi and Preece, Alun , year =. Sanity Checks for Saliency Metrics , eprint=

  35. [43]

    Explainable artificial intelligence (

    Vale, Daniel and El-Sharif, Ali and Ali, Muhammed , year =. Explainable artificial intelligence (. AI and Ethics , volume =

  36. [44]

    2021 , title =

    Nature Machine Intelligence , volume =. 2021 , title =

    2021

  37. [45]

    Counterfactual Explanations without Opening the Black Box:

    Wachter, Sandra and Mittelstadt, Brent and Russell, Chris , year =. Counterfactual Explanations without Opening the Black Box:. Harvard Journal of Law & Technology , volume =. 1711.00399 , archivePrefix=

    arXiv

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Reviewed August 7, 2026 · model on record in the stance chip above.