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REVIEW 3 major objections 5 minor 179 references

Human-Centered Explainability in Interactive Information Systems: A Survey

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A review of 100 empirical user studies claims that explainability in interactive information systems can be mapped onto five definition dimensions, a three-axis design classification, and six measurement categories.

desk verdict A useful survey of 100 user studies on explainability that needs a revision to fix its coding-frequency arithmetic and publish its data. read the letter →

arxiv 2507.02300 v1 pith:VTMOUZFL submitted 2025-07-03 cs.HC

Yuhao Zhang , Jiaxin An , Ben Wang , Yan Zhang , Jiqun Liu This is my paper
classification cs.HC
keywords explainabilityhuman-centeredXAIsystematicreviewuserstudyinteractiveinformationsystemsexplanationdesignevaluationPRISMA
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

This survey claims that the scattered research on human-centered explainability in interactive information systems can be organized into a single structured picture based on 100 empirical user studies. It identifies five dimensions used to define explainability — where, why, to whom, what, and how — and shows that explainability sits between the content-focused concept of explanation and the broader goals of explainable AI. It classifies explanation interface designs by interactivity, modality, and interface type, and groups the constructs researchers measure into six categories: intrinsic, format and presentation, usability, experiential, ethics, and interaction with the explanation. If the picture is right, researchers can align definitions with design and evaluation choices instead of treating them separately, and future work can target the gaps the review flags, such as underused voice and video explanations and sparse ethical measurement.

What carries the argument

The structural encoding approach is the paper's carrier of the argument: a set of codebooks applied to the 100 included articles. Definitions were analyzed textually, grouping keywords into where/why/whom/what/how; designs were coded along interactivity, modality, and interface type; and measured constructs were collapsed into six categories. This taxonomy is what lets the review claim that definition, design, and evaluation are systematically mappable rather than idiosyncratic across studies.

What would settle it

A fresh systematic search covering user studies published after February 2024, using the same inclusion criteria but screening all search results rather than only top hits, would falsify the taxonomy if it found a substantially different distribution of definition dimensions, design categories, or measurement categories — for example, if voice or video explanations became common, or if many measured constructs fell outside the six reported categories.

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Extended reading notes

Core claim

The paper's central claim is that the empirical literature on explainability in interactive information systems, though terminologically inconsistent, converges on a tractable structure. Through a PRISMA-guided search and structural coding of 100 included articles (121 user studies), the authors find that definitions of explainability recur across five dimensions (where, why, whom, what, how); that explanation designs can be classified by interactivity, modality, and interface type; and that measurements of explainability itself fall into six user-centered categories. The paper argues that these three planes — definition, design, evaluation — are currently disconnected, and that making definitional dimensions explicit would let design and evaluation choices follow from them.

Load-bearing premise

The claim rests on the assumption that the 100 included papers fairly represent the whole body of empirical explainability user studies, even though the search screened only Google Scholar's top results and stopped in February 2024.

Editorial extensions

If this is right

  • Researchers can use the five definition dimensions as a checklist for stating what explainability means in a new study, making definitions comparable across projects.
  • Explanation interface designs can be reported along interactivity, modality, and interface type, enabling more direct comparison of user studies.
  • Evaluation of explainability itself can be separated from evaluation of its downstream effects, clarifying what a given metric actually measures.
  • The review's gap analysis points to concrete under-explored areas: voice and video modalities, VR and wearable interfaces, and ethics-related measurement.
  • Future work can adopt a definition-driven pipeline where design and evaluation choices follow from explicit answers to where, why, to whom, what, and how.

Reading between the lines

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

  • If the taxonomy generalizes, it gives future researchers a ready-made reporting template: state where, why, to whom, what, and how in definitions, then choose design and measurement accordingly; the paper hints at this but stops short of prescribing it.
  • The finding that objective interaction metrics form a distinct category suggests that as conversational and generative interfaces grow, log-based measures such as explanation initiation and time spent could become the dominant way to evaluate explainability, a trend the February 2024 cutoff cannot confirm.
  • A testable extension would be to apply the same codebooks to a post-2024 sample of large-language-model-based explanation user studies, to see whether the six measurement categories still fit or whether new categories such as output verifiability or hallucination awareness emerge.
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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 manuscript presents a PRISMA-based systematic review of 100 empirical user studies on explainability in interactive information systems. It derives three sets of contributions: five definition dimensions (where, why, whom, what, how), a design classification (interactivity, modality, interface type), and six measurement categories (intrinsic, format and presentation, usability, experiential, ethics, interaction with the explanation). The paper reports frequency distributions for these categories, discusses gaps and implications, and outlines future directions including explainability for LLM/GenAI systems.

Significance. If the reported data are reliable, the paper offers a genuinely useful synthesis: it consolidates a scattered literature, provides explicit search queries and codebooks, and separates the measurement of explainability itself from outcome measurement. Strengths include the detailed coding protocol, the inclusion of both information-science and HCI venues, and the identification of under-explored areas such as voice-based and video-based explanations. The main weakness is that several quantitative summaries are internally inconsistent, and the supporting data are not available; as printed, the empirical grounding of the taxonomies cannot be fully verified.

major comments (3)
  1. [4.4.1, Table 5] The frequency distributions supporting the six measurement categories are internally inconsistent. In the Usability row, the seven listed dimensions have counts 10+4+3+3+3+2+2 = 27, contradicting the header N=26, and the stated percentages sum to 103.8%. In the Ethics row, the counts 7+4+3 = 14 match N=14, but Scrutability is reported as 23.1% even though 3/14 = 21.4%, and the percentages sum to 101.7%. A similar inconsistency appears in Section 4.1.1, where 22 studies are said to specify AI-knowledge categories whose counts (4, 12, 27) sum to 43. Because the measurement taxonomy is a central empirical claim, these arithmetic errors must be corrected and the underlying coding data made available so the reader can distinguish typographical mistakes from coding errors.
  2. [4.3, interface-type paragraph] The interface-type frequencies are not internally consistent. The text reports 44 interactive-interface studies and says the vast majority were GUI with N=51; however, the enumerated GUI subtypes (dashboards 21, desktop applications 5, chatbots 4, web-based tools 4, mobile apps 3, pop-up messages 2, computer programs 1, VR applications 1, games 1) sum to 42, and adding the one VUI yields 43, with two additional studies listed as unspecified. These figures cannot all be correct, so the quantitative description of the design classification requires recomputation.
  3. [3.3-3.4] The review does not provide a list of the 100 included articles, a link to the Google Sheet used for coding, or a PRISMA flow diagram reporting the number of records screened and excluded at each stage. This prevents verification of the inclusion criteria and of the frequency claims in Tables 5 and 6, and falls short of the transparency expected of a PRISMA review. The authors should include the full list of included studies and make the coded data available as supplementary material.
minor comments (5)
  1. [2.1] The citation of [93] for the concept of scope is incorrect: [93] is a user study on intelligent vehicles, not a taxonomy source; the intended reference is likely [28] or [29].
  2. [Table 1] There is a typo in the Generic domain row: 'pfishing attack' should be 'phishing attack'.
  3. [Figures 4 and 5] Figures 4 and 5 have identical captions ('Examples of explainability modalities') even though they illustrate different content; the caption for Figure 5 should be updated to reflect interface types or modality-interface pairings.
  4. [3.1 and 5.3] The limitations section acknowledges the February 2024 cutoff but does not discuss the Google Scholar top-N screening strategy (initially top 20, expanding to 50/100 only when relevant items appeared), which is a more direct threat to sample representativeness; this should be acknowledged explicitly.
  5. [3.3-3.4] The coding protocol mentions dual-coding and cross-checking but reports no inter-coder reliability statistic (e.g., Cohen's kappa); reporting agreement would strengthen confidence in the taxonomy.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the survey's taxonomies are inductive codings of the reviewed literature, and its self-citations are minor and non-load-bearing.

full rationale

This paper is a systematic literature review and taxonomy synthesis, not a derivation with predictive claims. The five definition dimensions, the design classification, and the six measurement categories are produced by coding the 100 included articles using a codebook that partly refers to external prior frameworks (Section 3.4), and the frequency tables in Section 4 are descriptive statistics over that coded corpus. There is no fitted parameter that is later renamed as a prediction, and no definition is constructed in terms of the paper's own conclusions. Jiqun Liu is cited in several places, for example [166, 167, 168, 176, 177, 178], and some of those citations are to the authors' own prior work, but none carries the central load: the survey's classification claims would stand unchanged if those citations were removed. The internal arithmetic inconsistencies in Table 5 and Section 4.3 highlighted by the skeptic are an accuracy and reliability concern, not a circularity concern, so they do not change this verdict. The score of 1 reflects the presence of non-load-bearing self-citations rather than any circular derivation.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

This survey introduces no free parameters or invented entities. It rests on two domain assumptions: that the literature sample is representative, and that the qualitative coding is reliable. Both are standard for systematic reviews but are load-bearing for the specific frequency counts and taxonomies presented.

assumptions (2)
  • domain assumption The search and screening process produced a representative sample of empirical user studies on explainability in interactive information systems.
    The survey's conclusions generalize from 100 articles; if the sample is biased due to top-N screening or date cutoff, the findings may not represent the field (Section 3.1, 5.3).
  • domain assumption The authors' qualitative coding reliably captures the definitions, designs, and measurements in the included papers.
    The taxonomies are based on manual coding with cross-checking but no inter-coder reliability metric is reported, so the reproducibility of the categories is unverified (Section 3.4).

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Cite this review

Pith. "Pith review of Human-Centered Explainability in Interactive Information Systems: A Survey." pith.science (2026). https://pith.science/paper/VTMOUZFL

@misc{pith2026250702300,
  author       = {Pith},
  title        = {Pith review of: Human-Centered Explainability in Interactive Information Systems: A Survey},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/VTMOUZFL}},
  note         = {Machine review of arXiv:2507.02300}
}
read the original abstract

Human-centered explainability has become a critical foundation for the responsible development of interactive information systems, where users must be able to understand, interpret, and scrutinize AI-driven outputs to make informed decisions. This systematic survey of literature aims to characterize recent progress in user studies on explainability in interactive information systems by reviewing how explainability has been conceptualized, designed, and evaluated in practice. Following PRISMA guidelines, eight academic databases were searched, and 100 relevant articles were identified. A structural encoding approach was then utilized to extract and synthesize insights from these articles. The main contributions include 1) five dimensions that researchers have used to conceptualize explainability; 2) a classification scheme of explanation designs; 3) a categorization of explainability measurements into six user-centered dimensions. The review concludes by reflecting on ongoing challenges and providing recommendations for future exploration of related issues. The findings shed light on the theoretical foundations of human-centered explainability, informing the design of interactive information systems that better align with diverse user needs and promoting the development of systems that are transparent, trustworthy, and accountable.

Figures

Figures reproduced from arXiv: 2507.02300 by the authors.

Figure 1
Figure 1. Number of included articles Searches targeted titles, keywords, and abstracts in all databases except Google Scholar. For Google Scholar, the first and second authors utilized the "with all of the words" field to apply the search queries. We initially screened the top 20 results. If relevant articles not covered in other databases were identified, the screening was expanded to the top 50 and subsequently the top 100… view at source ↗
Figure 2
Figure 2. Literature search: Inclusion and exclusion criteria. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Definition keyword distribution in three definition terms. The colored texts indicate [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Examples of explainability modalities. Top Row (Left to Right): [ [PITH_FULL_IMAGE:figures/full_fig_p014_4.png]
Figure 5
Figure 5. Figure 5: Examples of explainability modalities. Top Row (Left to Right): [ [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.