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REVIEW 4 major objections 5 minor 137 references

Better Together? The Role of Explanations in Supporting Novices in Individual and Collective Deliberations about AI

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read Same AI explanations help groups and solo users differently

desk verdict A transparent, well-run qualitative study of a genuinely open question in XAI—the group/individual depth-versus-exchange trade-off holds up, but the causal 'explanations improved understanding' claim is weaker than the framing admits. read the letter →

arxiv 2411.11449 v3 pith:M256RGQD submitted 2024-11-18 cs.HC

Timothée Schmude , Laura Koesten , Torsten Möller , Sebastian Tschiatschek This is my paper · ORCID
classification cs.HC
keywords explainableAInovicesgroupdeliberationsharedunderstandingmodularexplanationsfocusgroupspublicsectordecisionconfidence
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 paper asks whether explanations of an AI system help people without technical backgrounds understand and deliberate about it differently when they work alone rather than in a group. Based on eight focus groups and twelve individual interviews using a modular set of 36 question-answer explanations about an employment-scoring algorithm, it claims that individual and group settings support different facets of understanding. Groups used the explanations to build shared understanding and to find arguments for and against deployment, while individuals engaged more deeply, performed better on factual study tasks, and said they missed exchanging views with others. The paper concludes that explainable AI design should treat the two settings as complementary rather than assuming one explanation format fits both.

What carries the argument

The central object is a modular, question-driven explanation collection: 36 question-answer pairs grouped into four information categories (data, system details, usage, and context), each subdivided into topics and three levels of detail, printed as physical A5 sheets that participants can sort, exchange, point to, and read aloud. The design lets users select information according to their interests, supports different levels of completeness and soundness, and is intended to work for both solo reading and collaborative interaction. The analysis maps participants' interactions onto known mechanisms of collaborative success and failure and onto facets of understanding, which lets the paper argue that each setting activates different facets and that explanations and social dynamics jointly determine whether groups reach a working understanding or abandon it.

What would settle it

Run the same explanation phase with a pre/post factual understanding test plus an information-scope question, comparing individuals and groups against a no-explanation control; if verbal claims of improved understanding appear without measured information gain, or appear equally with unrelated material, the calibrating-understanding account fails.

Watch

Extended reading notes

Core claim

Individual and group settings provide different grounds for understanding AI systems: groups realize cognitive and social mechanisms of collaborative success that produce shared understanding, while individuals develop focused, self-directed engagement that supports applying information to tasks. With the same question-driven modular explanation design, participants in groups located information together, shared it, debated interpretations, and delegated difficult material to more competent members, whereas solo participants read more intensively, requested comparable or more explanations, and calculated precise answers that no focus group completed. The paper also finds that explanations feed deliberation: groups used them to source reasoned arguments and to surface productive disagreement, while solo participants used them for internal deliberation and several changed their deployment decisions. At the same time, a concurrence-seeking dynamic resembling aspects of groupthink led one group to follow a minority position, showing that social dynamics can override explanation content. To reconcile mostly unchanged self-reported understanding with participants' verbal claims of improvement, the paper introduces a post-hoc process called calibrating understanding, in which people judge their understanding relative to the information they now know exists.

Load-bearing premise

The conclusion that explanations improved participants' understanding rests on the assumption that their verbal claims of better understanding reflect genuine learning rather than politeness or confusion, since the paper's calibrating-understanding mechanism is introduced after the fact and is not independently measured.

Editorial extensions

If this is right

  • Explanation designs for AI novices should not assume one format fits both solo and group deliberation; group settings need supports for shared understanding and argumentation, while solo settings need ways to compensate for the missing exchange of perspectives.
  • Because individuals outperformed groups on factual study tasks, deployment decisions that hinge on technical details may be better prepared individually before being discussed collectively.
  • The same modular explanation collection can support deliberation without a group: solo participants used it for internal deliberation, and several changed their deployment decisions after reading the materials.
  • Group outcomes depend on the social dynamic as much as on the explanations: familiar, trusting groups bridged individual understanding gaps, while groups with low trust or discouragement abandoned understanding.
  • Explanations that supply all four information categories give groups material for reasoned arguments and disagreement, but they do not by themselves prevent concurrence-seeking behavior such as the groupthink-like pattern observed in one focus group.

Reading between the lines

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

  • If calibrating understanding is real, self-report-only evaluation of explainable AI will systematically understate explanation benefits; future studies should measure perceived information scope alongside self-reported understanding.
  • The finding that individuals solved tasks better while groups deliberated better suggests a two-phase format of individual preparation followed by group deliberation, which the paper suggests as an ideal combination but does not itself test.
  • A testable extension would give the same modular explanations to groups with a structured opposing role, since the paper attributes the concurrence-seeking outcome partly to the absence of a devil's advocate voice.
  • The physical A5 format may itself matter, because shared understanding relied on sorting, exchanging, and pointing at sheets; whether these benefits survive a digital version is an open question.
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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

4 major / 5 minor

Summary. This paper presents a task-based interview study (8 focus groups and 12 single interviews; 43 AI-novice participants) examining how a modular, question-driven explanation design—36 question–answer pairs about the Austrian AMS employment-prediction algorithm, organized into data, system details, usage, and context—supports understanding and deliberation in individual versus collective settings. The authors combine before/after self-reports, four factual task questions, and thematic analysis of transcripts. They report that groups used explanations to build shared understanding, source arguments, and sometimes experienced process loss, while individuals engaged more deeply and performed better on the tasks; self-reported understanding mostly stayed flat, which the paper reconciles by introducing a 'calibrating understanding' construct. The paper closes with design recommendations for XAI for public deliberation.

Significance. If the central claims hold, this is a useful contribution to human-centered XAI for public-sector AI: it provides a rare empirical comparison of one-to-one versus many-to-one explanation use, includes decision-subject focus groups, and ships a transparent codebook, full explanation set, and task materials. The qualitative process findings—groups share, outsource, and argue; individuals focus and calculate—are well illustrated and likely to inform future design. However, the causal component ('explanations improved understanding') is not established by the quantitative measures, and the task-performance comparison is confounded. The value of the paper is therefore mainly in the descriptive process account and design implications, which are credible after the claims are appropriately narrowed.

major comments (4)
  1. [§4.1.3, §5.2, Table 3] The conclusion that the explanations improved understanding rests on the post-hoc, unmeasured construct 'calibrating understanding.' The quantitative self-reports are mostly flat (e.g., 9 of 12 single-interview participants report no change, and many focus-group participants do too), and there is no no-explanation control. The verbal reports used to instantiate calibration are the measures most vulnerable to social desirability and to ambiguity about the meaning of 'understanding.' Because the calibration process is not independently assessed, the flat self-reports cannot simply be set aside. Please either operationalize calibration (e.g., the information-scope rating suggested in §5.2) and collect it in a follow-up, or revise the abstract and conclusion to state that explanations were perceived as helpful and supported deliberation processes, not that they demonstrably improved understanding.
  2. [§4.1.3, Tables 1–3, §6] The claim that 'participants in single interviews performed better in the study tasks' is confounded by education and recruitment. The single-interview sample is predominantly university-educated (11 of 12), whereas the focus-group sample includes more vocational and secondary-school participants. The manuscript acknowledges the imbalance in §6 but does not report the promised comparison restricted to university-educated participants, and task performance was not measured before the explanation phase. The observed difference may reflect pre-existing knowledge or education rather than the social setting.
  3. [§3.3.1–3.3.2] The procedural asymmetry between settings undermines the comparative task-performance result. Groups had 15 minutes of orientation, 15 minutes of tasks, and a separate 10-minute group decision phase, while individuals had 20 minutes of orientation and 20 minutes of tasks with no decision phase. Focus-group participants may have spent task-phase time discussing rather than answering, and the collective decision phase could have changed their later engagement. The comparative interpretation should either treat task scores as descriptive only or analyze performance on a comparable time/phase basis.
  4. [§4.1.5, §5.2] The headline comparative finding—individual and group settings support different understanding facets—is well supported as a qualitative account. However, the conclusion then asserts that 'the explanations had a positive effect on understanding' (also echoed in the abstract). This stronger causal statement is not load-bearing for the facet-difference finding and should be separated from it: the process data support claims about how explanations were used, not that the explanation phase caused a measurable increase in understanding.
minor comments (5)
  1. [§3.2.1, §3.2.2, Figure 1] The level labels are inconsistent: §3.2.1 says 'base level, level 2, level 3,' while §3.2.2 says 'base level, level 1, level 2'; Figure 1 uses 'Base/Level 2/Level 3.' Please standardize the nomenclature throughout.
  2. [§5.3] The reference to 'P3' appears to be an erroneous participant label; all other participant labels in the paper are of the form S1–S12 or focus-group IDs (e.g., S3), so please correct this reference.
  3. [Table 4] Table 4 is hard to read because the three decision columns are not clearly separated; consider restructuring it so Decision I, Group Decision, and Decision II are visually distinct and the color coding is described in a print-accessible way.
  4. [Table 1] Table 1 lists F2's education as 'n/a'; if these data are missing, state that explicitly in the table note rather than leaving a bare value.
  5. [§3.4.1] There is a typo/capitalization error in the sentence beginning 'Thus, The article served as...'—'The' should be lowercase.

Circularity Check

0 steps flagged · score 1.0 of 10

No circular derivation: findings rest on observed interactions, task performance, and self-reports; self-citations only motivate the explanation design.

full rationale

This is an empirical observational interview study, not a derivation. The explanation design (categories data, system details, usage, context) is inherited from the authors' earlier information-needs work [107], and prior work [106] informs framing, but the paper's central claims—that groups create shared understanding and source arguments while individuals engage more deeply and perform better on tasks—are grounded in transcripts, task scores, and self-reports (Tables 3 and 4, excerpts in Sections 4.1 and 4.2). No equation or fitted parameter is later renamed as a prediction, and there is no formal chain where an output is identical to an input by construction. The 'calibrating understanding' construct (Section 5.2) is introduced post hoc to reconcile flat self-reported understanding with verbal reports of improvement; this is an interpretive assumption and a validity threat, but it is not circular because the construct is not defined in terms of the conclusion and the conclusion does not follow by construction from the construct. The paper explicitly acknowledges the incongruence and suggests additional measures, showing honesty about the limitation. The self-citations are not load-bearing: [107] supplies design inputs, not the empirical findings, and [106] is cited as related work. The paper even reports contrary evidence (e.g., Group A's groupthink-like decision and lower group task performance), showing the findings could have contradicted the framing. The absence of a no-explanation control and the education imbalance between samples are validity concerns, not circularity. No significant circularity is present.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no free parameters or invented entities. It relies on established qualitative and psychological frameworks as interpretative lenses. The main epistemic load is carried by the assumption that participants' verbal reports and observed interactions validly reflect cognitive processes, an assumption the authors partially problematize through the 'calibrating understanding' construct.

assumptions (5)
  • domain assumption The six facets of understanding from Wiggins and McTighe (2005) are a valid operationalization of understanding for this study.
    Section 2.3.1 and Section 3.5.2 adopt the facets framework to compare individual and group understanding; if the facets are not a valid measure, the comparative conclusions about understanding facets are unsupported.
  • domain assumption The cognitive and social mechanisms of collaborative success and failure from Nokes-Malach et al. (2015) apply to in-person focus groups.
    Sections 4.1.1 and 4.1.2 map observed interactions onto this framework; the mapping assumes the framework transfers from controlled learning studies to the study's context.
  • domain assumption The elements of deliberation from Stromer-Galley (2007) are a valid coding scheme for identifying deliberation in group discussions.
    Section 3.5.3 and Section 4.2 use this scheme to classify arguments, opinions, and disagreement; if the scheme is inapplicable, the deliberation findings lack a defined standard.
  • domain assumption Self-reported understanding on a 5-point scale reflects a meaningful psychological construct, despite the observed calibration effect.
    The paper's own data show self-reports can stay flat while verbal reports indicate learning; the post-hoc 'calibrating understanding' mechanism assumes the scales still carry meaning.
  • domain assumption The AMS employment-scoring use case is representative enough of public-sector AI systems for the design suggestions to transfer.
    Section 3.1.2 argues the AMS algorithm represents a class of public AI systems; Section 6 acknowledges the specific sociotechnical context may limit transferability.

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Cite this review

Pith. "Pith review of Better Together? The Role of Explanations in Supporting Novices in Individual and Collective Deliberations about AI." pith.science (2026). https://pith.science/paper/M256RGQD

@misc{pith2026241111449,
  author       = {Pith},
  title        = {Pith review of: Better Together? The Role of Explanations in Supporting Novices in Individual and Collective Deliberations about AI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/M256RGQD}},
  note         = {Machine review of arXiv:2411.11449}
}
read the original abstract

Deploying AI systems in public institutions can have far-reaching consequences for many people, making it a matter of public interest. Providing opportunities for stakeholders to come together, understand these systems, and debate their merits and harms is thus essential. Explainable AI often focuses on individuals, but deliberation benefits from group settings, which are underexplored. To address this gap, we present findings from an interview study with 8 focus groups and 12 individuals. Our findings provide insight into how explanations support AI novices in deliberating alone and in groups. Participants used modular explanations with four information categories to solve tasks and decide about an AI system's deployment. We found that the explanations supported groups in creating shared understanding and in finding arguments for and against the system's deployment. In comparison, individual participants engaged with explanations in more depth and performed better in the study tasks, but missed an exchange with others. Based on our findings, we provide suggestions on how explanations should be designed to work in group settings and describe their potential use in real-world contexts. With this, our contributions inform XAI research that aims to enable AI novices to understand and deliberate AI systems in the public sector.

Figures

Figures reproduced from arXiv: 2411.11449 by the authors.

Figure 1
Figure 1. Overview of explanations. Explanations were designed as a collection of 36 question-answer pairs. The questions were assigned to 4 categories, data, system details, usage, and context, each containing 9 questions. Participants received the base explanations at the beginning of the explanation phase, as indicated by the ticked boxes, and could request all other explanations at any time during the explanation phase us… view at source ↗
Figure 2
Figure 2. Four explanation examples. Examples for explanations in the categories data, system details, usage, and context. Each question was printed on a sheet of A5 paper with a short answer to the question. Answers could be fully textual or complemented with visual elements like charts or colored shapes. Each category was given a different color and icon to facilitate navigation [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Overview of the study procedure. Focus groups and single interviews differed only in the explanation phase and the questions for the second individual reports [PITH_FULL_IMAGE:figures/full_fig_p009_3.png] view at source ↗
Figures from the paper (12 more)
Figure 4
Figure 4. Figure 4: Material for individual reports of participants. Participants received the materials for individual reports on laminated paper slips in different colors (blue, yellow, red, green, and grey) and used them to answer questions individually. Slips that were numbered with l…
Figure 5
Figure 5. Figure 5: Both explanation and social dynamic have an impact on collaborative performance. In focus groups, both explanations and social dynamic were key factors for the understanding outcome. If participants could engage easily with the explanations and each other, their intera…
Figure 6
Figure 6. Figure 6: Number of explanations requested. The left side shows explanations requested by focus groups, the right side by participants in single interviews. The horizontal lines indicate the median. While groups were able to process many explanations by splitting the reading, se…
Figure 7
Figure 7. Figure 7: Most helpful explanation categories for understanding and most influential categories for participants’ decisions. Participants could select any number of explanation categories for both questions, including none and all four. Focus group participants found all categor…
Figure 8
Figure 8. Figure 8: Summary of implications regarding the design of explanations for individual and collaborative settings based on our findings. 6 LIMITATIONS Like any research, this study had limitations. Due to the limited sample size, we did not analyze the impact of sex and/or gender…
Figure 9
Figure 9. Figure 9: Mock newspaper article. Participants received initial information about the employment prediction algorithm in the form of a mock newspaper article. The article provided key information and featured the perspectives of employers and employee associations [PITH_FULL_IM…
Figure 10
Figure 10. Figure 10: Study task. After the first exploration phase with the explanations, participants received a fictional job-seeker case example describing Mr. Harald G.10: A fictional job-seeker with a brief backstory and a list of features that would be used to calculate his employme…
Figure 11
Figure 11. Figure 11: Explanation overview [PITH_FULL_IMAGE:figures/full_fig_p041_11.png]
Figure 12
Figure 12. Figure 12: Data [PITH_FULL_IMAGE:figures/full_fig_p042_12.png]
Figure 13
Figure 13. Figure 13: System details [PITH_FULL_IMAGE:figures/full_fig_p043_13.png]
Figure 14
Figure 14. Figure 14: Usage [PITH_FULL_IMAGE:figures/full_fig_p044_14.png]
Figure 15
Figure 15. Figure 15: Context [PITH_FULL_IMAGE:figures/full_fig_p045_15.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.