Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems

Ahsan Zaman; Akriti Pandey; Maria J. P. Peixoto; Peter R. Lewis

REVIEW 3 major objections 5 minor 28 references

Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems

T0 review · 3 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read This paper argues that the XAI evaluation literature is built around sighted users: only 1 of 79 end-user studies includes participants with visual impairment, and the dominant techniques default to visual formats.

desk verdict A useful, honestly-reported gap analysis and prototype for accessible XAI, but the headline statistic rests on a search string that may miss relevant work and the user study has three participants. read the letter →

arxiv 2508.10806 v1 pith:QHWU6TWF submitted 2025-08-14 cs.AI

Maria J. P. Peixoto , Akriti Pandey , Ahsan Zaman , Peter R. Lewis This is my paper

classification cs.AI

keywords explainableAIaccessibilityvisualimpairmentnon-visualexplanationsSHAPLIMEuserevaluationinclusivedesign

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 sets out to show that explainable AI has an accessibility gap: across 79 end-user evaluation studies published from 2019 to 2024, only one includes participants with any visual impairment, and only three mention accessibility at all. The dominant techniques—SHAP, LIME, Grad-CAM, decision trees—are evaluated and displayed in visual formats such as bar charts, heatmaps, and graphs, so the field's evidence about 'understandable AI' is built almost entirely on sighted users. To show the gap can be closed, the authors build a screen-reader-friendly traffic-prediction prototype offering simplified and detailed LIME and SHAP explanations, then assess it with accessibility experts and three users with lived experience of sight loss. Their preliminary finding is that simplified explanations are understood better than detailed ones by non-visual users, and that multimodal presentation is needed for equitable interpretability.

What carries the argument

The argument runs on two machines. First, a systematic review following a PRISMA flow diagram, with search string (XAI OR "AI explanation") AND (end-user OR user) AND (evaluation OR validation) across seven databases, whose exclusion criteria leave 79 studies; this supplies the 1-in-79 and 3-in-79 counts that define the gap. Second, a four-part methodological proof of concept—categorization of AI systems, persona definition, prototype implementation, expert and user assessment—that turns the gap into a testable design: a web prototype with a machine-learning traffic-flow model and simplified and detailed LIME and SHAP explanations, made accessible through screen readers, keyboard navigation,

What would settle it

Run the same seven-database review with search terms expanded to include 'screen reader,' 'assistive technology,' 'visual impairment,' 'accessible explanation,' and 'non-visual'; if this surfaces more than a handful of end-user XAI evaluations that include blind or low-vision participants, the claimed 1-in-79 gap is overstated. Separately, a comprehension experiment with a larger sample comparing simplified and detailed screen-reader LIME and SHAP explanations would test the preliminary finding.

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Extended reading notes

Core claim

The central claim is that accessibility, specifically for people with vision impairments, is missing from the empirical XAI literature and from the default design of XAI techniques. In a review of 79 end-user evaluation studies, only three mention accessibility concerns and only one includes participants who report visual impairment; 33 of the 79 evaluate SHAP and 30 evaluate LIME, whose typical outputs are visual. The paper argues that visual-only explanations block users from contesting biased AI decisions and exclude them from meaningful participation in AI governance. Based on a small co-design session, it further argues that simplified explanations outperform detailed ones for non-visua

Load-bearing premise

The review's count of only one accessibility-aware study in 79 rests on the assumption that the search string (XAI OR "AI explanation") AND (end-user OR user) AND (evaluation OR validation) captures the whole field of end-user XAI evaluation studies, so a gap in vocabulary would not masquerade as a gap in research.

Editorial extensions

If this is right

If the review's counts hold, published claims about how well users understand SHAP, LIME, and other XAI techniques apply to sighted users only; generalizing them to the roughly 2.2 billion people with near or distance vision impairment is unsupported.
XAI technique choice would have to treat accessibility as a first-class criterion alongside model-agnosticism and fidelity, since the default outputs of the most popular methods are visual.
Explanation design for non-visual users should offer simplified overviews first and detailed content as an option; the preliminary result says details without an accessible overview hurt comprehension.
Multimodal presentation—text, audio, simplified point form, linear charts over tables—becomes a requirement for equitable access, not an enhancement.
The four-part proof of concept offers a reusable template for evaluating any AI system's explanations with disabled users before deployment.

Reading between the lines

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

A testable extension: re-run a standard task-based XAI evaluation, such as decision accuracy with SHAP versus LIME, using screen-reader output and compare outcomes to published sighted results; if accuracy gaps appear, existing XAI metrics may be measuring visual literacy as much as understanding.
The preliminary finding implies that adding alt-text or image descriptions to existing charts may not be enough; non-visual comprehension may require a fundamentally different representation, such as sonified feature weights or narrative text.
If the review's gap is real, it also bears on transparency regulation: an explanation that cannot be perceived is arguably no explanation at all, so accessibility standards may need to be read into AI transparency duties.
Because the review used one narrow search string, a broader replication using terms like 'screen reader,' 'assistive technology,' and 'accessible explanation' would show whether the gap is in the literature or partly in the search vocabulary.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Short version: this paper gives us a concrete number for a gap we all suspected—XAI evaluation is heavily visual and almost no one includes users with sight loss—and it reports its own work honestly. But the number (1 of 79 studies) rests on a search string that is probably too narrow, and the user study is three people. Don't take the number as gospel, but do take the paper seriously as a proof of concept.

What's genuinely new: the PRISMA review of 79 end-user XAI evaluation studies, with a table breaking down techniques, metrics, and participant groups. As far as I know, no one has tabulated that specific slice before. The finding that only one study includes participants with a visual impairment (and even that one doesn't adapt the explanations) is a useful, citable piece of evidence. The prototype itself is also a real contribution: a working traffic-flow dashboard with LIME/SHAP in simplified and detailed forms, built with screen-reader compatibility in mind and refined with input from CNIB experts. That's a good template for future work.

Soft spots, in order of size. First, the search string: (XAI OR "AI explanation") AND (end-user OR user) AND (evaluation OR validation). That presumes any accessible-XAI study would self-identify with those terms. A paper about non-visual explanations might say 'screen reader' or 'assistive technology' rather than 'XAI', and 'user study' rather than 'evaluation' or 'validation'. The authors don't report per-database search strings, truncation, or a protocol, and no coding reliability is given. So the 1-in-79 statistic is a lower bound on what the field is doing, not a precise measure. The qualitative claim—'rarely'—probably survives a broader search, but the specific number is fragile. This is the one part I'd want fixed before using the number in a paper.

Second, the co-design had three active participants. The authors are honest about this, and they call the result preliminary. That's fair. But the abstract still says 'preliminary findings suggest simplified explanations are more comprehensible,' which reads as a finding. I'd rather see that phrased as 'anecdotal' or 'hypothesis-generating.' The evaluation has no baseline, no quantitative measure, just qualitative feedback. That's fine for a proof of concept, not for a claim.

Minor: the prior survey from the same group (Nwokoye et al. 2024) is cited to motivate the gap, and the new review is independent but overlapping. It would have been tidy to state the relationship explicitly.

So: this is a paper for the XAI and accessibility communities. It deserves a serious peer review—the gap measurement is worth having, and the prototype is a useful design example. The literature review methodology needs to be tightened, and the user-study conclusions need to be softened or supported with a larger sample. If you're looking at whether to engage, I'd say yes—as a citable proof of concept, not as a definitive empirical claim.

Referee Report

3 major / 5 minor

Summary. The paper addresses accessibility in explainable AI (XAI), focusing on users with vision impairments. It reports a PRISMA-style literature review of 79 studies (2019–2024) that evaluate XAI techniques with end users, concluding that only one included study involves participants with visual impairment, three mention accessibility concerns, and most explanations rely on inherently visual formats. The authors then present a four-part methodological proof of concept: a categorization of AI systems, a persona (Caroline) created with CNIB input, a functional prototype implementing simplified and detailed LIME/SHAP explanations with screen-reader support, and an evaluation with accessibility experts and users with lived experience of sight loss. Based on three active participants, the user evaluation suggests that simplified explanations are more comprehensible than detailed ones for non-visual users and that multimodal presentation is needed. The authors explicitly label the user study preliminary.

Significance. If the prevalence claim is robust, the finding that only 1 of 79 XAI evaluation studies includes participants with visual impairment is important and actionable for the XAI and HCI communities. The proof of concept is concrete, involves collaboration with accessibility experts from CNIB, follows ARIA practices, and was tested with several screen readers; this provides a useful template for future inclusive XAI design. The paper is honest about the small sample and preliminary nature of the user results. Its main weakness is that the literature-review methodology and the modality-based conclusions are not reported in sufficient detail to fully support the central prevalence claim.

major comments (3)

[§2, search string and PRISMA flow] The single search string (XAI OR “AI explanation”) AND (end-user OR user) AND (evaluation OR validation) may systematically miss accessible-XAI studies that use different vocabulary, such as “screen reader”, “non-visual”, “blind users”, “assistive technology”, “low vision”, or “interpretable machine learning” combined with “user study” or “co-design”. Because the headline statistic (79 studies; 1 with visual-impairment participants) is the main evidence for the claimed gap, this is load-bearing. The paper does not report per-database search strings, truncation or date filters, inclusion/exclusion decision details, or screening reliability. The authors should expand and validate the search, report a protocol, and ideally add a citation/screening check against known accessible-XAI work; otherwise the 1/79 figure may overstate the field’s neglect.
[§2 and Table S1] The claim that “most explanations rely on inherently visual formats” is not supported by systematic modality coding. Table S1 records XAI techniques, evaluation methods, metrics, and evaluators, but it does not code whether each study’s explanations were visual-only, textual, audio, haptic, or multimodal. Inferring visual dependence from technique names (SHAP, LIME, Grad-CAM) conflates the default implementation with the actual presentation used in the study; LIME and SHAP can be rendered as text or speech. A separate analysis or an additional column in Table S1 that operationalizes “inherently visual” is needed before this general conclusion is stated.
[§3.4, user evaluation] The secondary claim—simplified explanations are more comprehensible for non-visual users than detailed ones—rests on three active participants and qualitative agreement, with no coding scheme, inter-rater reliability, or quantitative comparison. The authors correctly call this preliminary in the Conclusion, but the abstract presents it as a finding. Either report the evidence with the caveat directly in the abstract, or frame the user component as an illustrative co-design session rather than an empirical evaluation result. This does not invalidate the proof of concept, but it must not be read as a measured comparison of explanation formats.

minor comments (5)

[§3.1] The “randomly selected combination” of AI categories is not reproducible. Specify the random selection procedure or state explicitly that it was an arbitrary illustrative choice.
[§3.3, Algorithm 1] The pseudocode contains informal lines such as “Import lib imports”. Clean up the pseudocode and use consistent notation for functions and data structures.
[Table S1] The row for Gunning and Aha [2019] lacks a concrete evaluation method and metrics; use “N/A” or clarify what was extracted. Also, some evaluator counts are missing (“quantity not provided”); mark these consistently.
[§3.4] The expert evaluation with six CNIB experts is described only narratively. While useful, a short summary of how expert feedback was recorded and aggregated would improve transparency.
[References and footnotes] Some URLs appear as footnotes and some as reference entries; format consistently according to the venue style.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; the literature-review gap claim is independently supported by the paper's own PRISMA review, and the preliminary user finding is directly reported.

full rationale

The paper does not contain a mathematical derivation in which an output equals an input by construction. The central claim—that XAI evaluation studies rarely include disabled users and rely on visual formats—is an empirical summary of the 79 papers the authors screened (Section 2, Figure 1, Table S1). The search-string limitation (e.g., missing studies that use accessibility vocabulary) is a validity/correctness concern, not circularity. The only self-citation is to the authors' prior survey [Nwokoye et al., 2024], used to motivate the gap, but the gap is re-established by the new independent review, so the self-citation is not load-bearing. The preliminary finding that simplified explanations are more comprehensible is explicitly based on three participants' feedback and is labeled preliminary in Sections 3.4 and 4; it is not a fitted parameter renamed as a prediction. No uniqueness theorem, ansatz, or renaming of a known result is invoked. Therefore no circular step is exhibited; score reflects the minor non-load-bearing self-citation only.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The paper is empirical and design-oriented, so the 'free parameters' ledger is empty. The central claims rest on dataset-selection assumptions (search string coverage, absence-as-exclusion), on treating self-report as comprehension, and on a deliberately constructed persona. These are reasonable domain assumptions for a proof of concept but they are not independently verified.

assumptions (4)

domain assumption The search string (XAI OR 'AI explanation') AND (end-user OR user) AND (evaluation OR validation) captures the population of XAI evaluation studies.
Section 2. If this string misses accessible-XAI studies phrased differently, the claimed gap is overstated.
domain assumption A paper that does not mention accessibility or visual impairment did not consider disabled users.
Section 2. Absence of mention is treated as absence of inclusion; this is an interpretation, not a verified fact.
domain assumption Self-reported comprehension in a one-hour online co-design session is a valid measure of explanation comprehension.
Section 3.4. Participants were asked what information they extracted and which method they preferred; no objective comprehension test was administered.
domain assumption The single persona 'Caroline' adequately represents a lived-experience user for the scenario design.
Section 3.2. The authors explicitly state this is an illustrative persona, not a representation of all sight-loss profiles.

invented entities (1)

Persona 'Caroline' (traffic manager, totally blind since birth)
purpose: Grounds the case study and evaluation context for non-visual XAI interaction.
An illustrative design construct created with CNIB experts; it is not a measured entity and is acknowledged as not representative of all sight-loss users (Section 3.2).

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Cite this review

Pith. "Pith review of Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems." pith.science (2026). https://pith.science/paper/QHWU6TWF

@misc{pith2026250810806,
  author       = {Pith},
  title        = {Pith review of: Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QHWU6TWF}},
  note         = {Machine review of arXiv:2508.10806}
}

read the original abstract

As AI systems are increasingly deployed to support decision-making in critical domains, explainability has become a means to enhance the understandability of these outputs and enable users to make more informed and conscious choices. However, despite growing interest in the usability of eXplainable AI (XAI), the accessibility of these methods, particularly for users with vision impairments, remains underexplored. This paper investigates accessibility gaps in XAI through a two-pronged approach. First, a literature review of 79 studies reveals that evaluations of XAI techniques rarely include disabled users, with most explanations relying on inherently visual formats. Second, we present a four-part methodological proof of concept that operationalizes inclusive XAI design: (1) categorization of AI systems, (2) persona definition and contextualization, (3) prototype design and implementation, and (4) expert and user assessment of XAI techniques for accessibility. Preliminary findings suggest that simplified explanations are more comprehensible for non-visual users than detailed ones, and that multimodal presentation is required for more equitable interpretability.

Figures

Figures reproduced from arXiv: 2508.10806 by the authors.

**Figure 1.** PRISMA Flow Diagram to reduce barriers and ensure that individuals with sight loss and other disabilities can access and participate in the design and development of AI-based systems, particularly concerning their ability to understand the decisions and recommendations provided by these applications. However, the sight loss community has been largely overlooked when it comes to access to AI explanations [Nwokoye e… view at source ↗

**Figure 2.** Prototype created for the urban traffic scenario [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 5.** Default explanation generated from SHAP - Detailed Ex [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

Figures from the paper (1 more)

**Figure 3.** Figure 3: XAI techniques available in the prototype. [PITH_FULL_IMAGE:figures/full_fig_p007_3.png]

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Reference graph

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Pith tools

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Comparing user perception of explanations developed with XAI methods

Jonathan Aechtner, Lena Cabrera, Dennis Katwal, Pierre Onghena, Diego Penroz Valenzuela, and Anna Wilbik. Comparing user perception of explanations developed with XAI methods. In 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pages 1–7. IEEE,

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Taha Falatouri, Mehran Nasseri, Patrick Brandtner, and Farzaneh Darbanian. Shedding light on the black box: Explainable AI for predicting household appliance failures. In Helmut Degen, Stavroula Ntoa, and Abbas Moallem, editors, HCI International 2023 – Late Breaking Papers , volume 14059, pages 69–83. Springer Nature Switzerland,

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Tarun Gupta, Libin Kutty, Ritu Gahir, Nnamdi Ukwu, Sayantan Polley, and Marcus Thiel. IRTEX: Image retrieval with textual explanations. In 2021 IEEE 2nd International Conference on Human-Machine Systems (ICHMS) , pages 1–4. IEEE,

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Lujain Ibrahim, Mohammad M Ghassemi, and Tuka Alhanai

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How can i choose an explainer?: An application-grounded evaluation of post-hoc explanations

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Sunnie S. Y. Kim, Nicole Meister, Vikram V. Ramaswamy, Ruth Fong, and Olga Russakovsky. HIVE: Evaluating the human interpretability of visual explanations. In Shai Avidan, Gabriel Brostow, Moustapha Ciss´ e, Giovanni Maria Farinella, and Tal Hassner, editors, Computer Vision – ECCV 2022 , volume 13672, pages 280–298. Springer Nature Switzerland,

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