arxiv: 2604.27997 · v1 · submitted 2026-04-30 · 💻 cs.HC

Recognition: unknown

When and How AI Should Assist Brainstorming for AI Impact Assessment

Jarod Govers , Sanja \v{S}\'cepanovi\'c , Daniele Quercia

Authors on Pith no claims yet

Pith reviewed 2026-05-07 07:50 UTC · model grok-4.3

classification 💻 cs.HC

keywords brainstormingassessmentimpactshouldduringparticipantsevaluatedideas

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The pith

AI assistance boosts brainstorming quality and perceptions in AI impact assessment for general-purpose applications but not for specialized ones.

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

The paper investigates how AI can support team brainstorming during AI impact assessment to prevent harm from AI systems. Researchers adapted structured methods from strategic foresight, co-designed AI interventions, and tested them in workshops. They found that AI improved the quality of impact assessments and how participants felt about the brainstorming process when applied to a general-purpose chatbot companion, but showed no such benefits for a specialized kidney allocation application. This matters because current AI tools for impact assessment are not designed for collaborative team settings, and understanding when to deploy AI hints versus full solutions can help teams capture diverse views more effectively.

Core claim

The central discovery is that AI should only offer hints and not complete solutions during early ideation phases, initiating interactions only when teams experience fixation or saturation; it should help structure ideas during convergence phases; leverage participant expertise to refine ideas; and primarily support tedious process tasks rather than the creative ideation that teams prefer to handle themselves.

What carries the argument

Co-designed AI interventions integrated into two structured methods adapted from strategic foresight, specifically tailored for collaborative brainstorming in AI impact assessment workshops.

Load-bearing premise

That the results observed in the ten workshops with 54 participants using just two specific AI cases will hold for other team configurations, different AI applications, and actual professional impact assessment practices.

What would settle it

Conducting additional workshops or real-world trials with a wider variety of AI applications and team compositions, then checking if the improvement in quality and perceptions appears only for general-purpose cases.

Figures

Figures reproduced from arXiv: 2604.27997 by Daniele Quercia, Jarod Govers, Sanja \v{S}\'cepanovi\'c.

**Figure 1.** Figure 1: Structured team brainstorming of AI impacts with AI interventions; consented photos from workshops in three sites. view at source ↗

**Figure 2.** Figure 2: Our methodology has three steps. In Step 1, we selected two brainstorming methods suited to AI impact assessment (§3.1, Step 1A), and identified types and points of AI intervention in these methods from prior work (§3.2, Step 1B). In Step 2, we elicited design requirements through five formative and co-design workshops (§4). In Step 3, we evaluated the resulting AI interventions across 10 workshops (§5). F… view at source ↗

**Figure 3.** Figure 3: An example from a FW workshop with the Final AI tool and its elements fulfilling the four design requirements. view at source ↗

**Figure 4.** Figure 4: Evaluation study workflow. Registration and pre-study questionnaires collect demographics and initial perceptions. view at source ↗

**Figure 5.** Figure 5: Broad guidance for AI interventions in brainstorming, based on the stage (when, view at source ↗

**Figure 6.** Figure 6: Full annotated breakdown of each module in our AI tool. The top left panel shows the initial design requirements gathered in our formative workshops, and the final requirements gathered in the co-design workshops. The top right panel shows how we built the initial requirements into the Initial AI tool. The bottom panels show how we built the final requirements into the Final AI tool view at source ↗

**Figure 7.** Figure 7: The description of the AI use for the chatbot companion workshops (Formative, and Full Study’s Workshops 1–9). view at source ↗

**Figure 8.** Figure 8: The description of the AI use for the medical kidney monitoring and transplant allocation AI use workshops (Verification view at source ↗

**Figure 9.** Figure 9: All workshops include a short 5–10 minute demo to help familiarise participants with the brainstorming method and view at source ↗

**Figure 10.** Figure 10: Persona selection/customisation screen for the Chatbot Companion (left) and Medical AI (right) Empathy Mapping view at source ↗

**Figure 11.** Figure 11: Screenshot of the interface for the Futures Wheel activity with AI interventions in Miro. view at source ↗

**Figure 12.** Figure 12: Screenshot of the interface for the Empathy Mapping activity with AI interventions in Miro. view at source ↗

**Figure 13.** Figure 13: Prolific expert annotation interface for an example mitigation. view at source ↗

**Figure 14.** Figure 14: Semantic similarity for AI-generated vs. human-generated benefits (left), risks (middle), and mitigations (right) view at source ↗

read the original abstract

A key task in AI practice is to assess potential impacts to prevent harm. Current AI tools assisting AI impact assessment have not been designed or evaluated for collaborative team brainstorming, and they do not capture the range of views in diverse teams. We studied how AI can support team brainstorming during AI impact assessment and made three contributions. First, we adapted two structured methods from strategic foresight and co-designed AI interventions for them in five in-person workshops with 28 participants in total. Second, we evaluated the interventions in ten in-person workshops with 54 participants, finding that AI improved impact assessment quality and brainstorming perceptions for a general-purpose AI use (a chatbot companion) but not for a specialised one (a kidney allocation application). Third, our findings result in broader design guidance for AI assistance in brainstorming: AI should only offer hints and not solutions during early ideation, initiating interaction only when participants face fixation or saturation; it should facilitate structuring ideas during convergence; leverage expertise to refine ideas; and overall, it should serve more in support of tedious brainstorming process tasks, rather than ideation that teams value to do themselves.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

Workshops give some practical guidelines for AI in impact assessment brainstorming, but the claim that it helps general uses more than specialized ones rests on two mismatched test cases.

read the letter

The main thing here is that the paper runs a series of workshops to test AI interventions in team brainstorming for AI impact assessment and turns the results into concrete design rules, but the key contrast between general and specialized AI uses is hard to interpret because the two examples differ in domain, stakes, and expertise demands. They adapted foresight methods, co-designed AI features with 28 participants across five workshops, then evaluated them with 54 people in ten more sessions. AI appeared to raise assessment quality and positive views for the chatbot companion scenario but showed no benefit for the kidney allocation tool, which produced guidelines like offering only hints early on, stepping in during fixation, aiding structure in later stages, and handling tedious tasks while leaving valued ideation to the team. This is new in its focus on collaborative harm-prevention work rather than solo ideation, and the co-design plus real-participant testing gives the guidelines some grounding that pure theory lacks. The approach is straightforward and the topic is relevant for tool builders. The soft spot is the comparison itself. The two cases vary along multiple dimensions beyond generality, with no additional matched examples or explicit controls mentioned, so the difference in outcomes cannot be cleanly attributed to the intended factor. That weakens how far the derived guidance travels. The sample size is modest and the abstract leaves measurement details on quality and analysis thin, though the full paper may fill some of that in. This is for HCI and responsible-AI researchers who design or study collaborative tools for ethics or foresight work. A reader in that area could pick up usable ideas even with the caveats. I would send it to peer review; the empirical effort is real and the topic needs this kind of testing, but the central contrast needs tightening before the claims land solidly.

Referee Report

2 major / 2 minor

Summary. The manuscript reports an empirical study on AI support for team brainstorming in AI impact assessment. It adapts two structured methods from strategic foresight, co-designs AI interventions in five workshops (28 participants), then evaluates them in ten workshops (54 participants total). The central finding is that AI assistance improved impact assessment quality and brainstorming perceptions for a general-purpose use case (chatbot companion) but not for a specialized one (kidney allocation application). From these results the authors derive design guidance: AI should offer hints rather than solutions in early ideation, initiate interaction only on fixation or saturation, facilitate structuring during convergence, leverage expertise to refine ideas, and primarily support tedious process tasks rather than ideation itself.

Significance. If the results hold after addressing confounds, the work supplies initial empirical evidence on the conditions under which AI can usefully assist collaborative impact assessment, a practically important task in responsible AI development. The in-person workshop format and use of established foresight methods are strengths that ground the design recommendations in observed team behavior. The study is modest in scale but directly addresses a gap in current AI tools for this activity.

major comments (2)

[Evaluation section and results describing the two applications and differential findings] The central claim that AI assistance is differentially effective for general-purpose versus specialized AI uses rests on a comparison of only two applications. These cases differ simultaneously in domain (consumer chatbot vs. medical/ethical allocation), potential harm, and likely participant domain knowledge, yet the analysis does not isolate generality/specialization from these confounds. No additional matched cases or explicit controls are described that would allow secure attribution of the observed difference to the intended factor. This directly undermines the design guidance derived in the third contribution.
[Abstract and Evaluation/Methods sections] The abstract and evaluation description provide no details on the operationalization of 'impact assessment quality,' including rubrics or criteria used, inter-rater reliability, blinding procedures, statistical controls, or how post-hoc interpretations of the specialized-case failure were validated. Without these, the reported improvement in one case but not the other cannot be rigorously assessed.

minor comments (2)

[Abstract] The abstract is information-dense; splitting the contributions and key findings into separate sentences would improve readability.
[Discussion or Conclusion] The manuscript would benefit from an explicit limitations subsection that directly addresses generalizability beyond the two tested applications and the workshop setting.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive and detailed feedback. We have reviewed each major comment carefully and provide point-by-point responses below, indicating where we will revise the manuscript.

read point-by-point responses

Referee: [Evaluation section and results describing the two applications and differential findings] The central claim that AI assistance is differentially effective for general-purpose versus specialized AI uses rests on a comparison of only two applications. These cases differ simultaneously in domain (consumer chatbot vs. medical/ethical allocation), potential harm, and likely participant domain knowledge, yet the analysis does not isolate generality/specialization from these confounds. No additional matched cases or explicit controls are described that would allow secure attribution of the observed difference to the intended factor. This directly undermines the design guidance derived in the third contribution.

Authors: We agree that the study compares only two applications and that these differ along multiple dimensions beyond generality versus specialization, including domain, potential harm, and likely participant domain knowledge. This limits our ability to attribute the differential outcomes solely to the intended factor. The applications were selected to contrast a general-purpose use case with a specialized one in realistic settings using established foresight methods, but we recognize the design does not include matched controls or additional cases. In the revised manuscript we will qualify the third contribution and derived design guidance as preliminary and exploratory rather than broadly generalizable. We will also expand the limitations section to explicitly discuss these confounds and outline how future studies could isolate the factor through additional matched cases or controlled designs. revision: yes
Referee: [Abstract and Evaluation/Methods sections] The abstract and evaluation description provide no details on the operationalization of 'impact assessment quality,' including rubrics or criteria used, inter-rater reliability, blinding procedures, statistical controls, or how post-hoc interpretations of the specialized-case failure were validated. Without these, the reported improvement in one case but not the other cannot be rigorously assessed.

Authors: We acknowledge that the abstract and the description in the evaluation section lack sufficient detail on the operationalization of impact assessment quality. While the full manuscript describes the workshop evaluation process, we will revise the abstract to include a concise summary of the quality metrics and substantially expand the Evaluation and Methods sections. The revisions will specify the rubrics and criteria applied, report inter-rater reliability (including any Cohen's kappa or equivalent measures), describe blinding procedures for independent raters, note any statistical controls, and explain how post-hoc interpretations of the specialized-case results were validated through team review and cross-checking. These additions will improve transparency and allow readers to better evaluate the differential findings. revision: yes

standing simulated objections not resolved

The study is complete and we cannot conduct additional workshops or add matched application cases to better isolate confounds; we will address this limitation by qualifying claims and expanding the discussion of limitations rather than collecting new data.

Circularity Check

0 steps flagged

Empirical HCI study with no derivation chain or self-referential reduction

full rationale

The paper describes an empirical process: adapting foresight methods, co-designing AI interventions in five workshops (28 participants), then evaluating them in ten workshops (54 participants) across two specific AI applications. Findings on differential effects (improved quality/perceptions for the chatbot case but not the kidney allocation case) and resulting design guidance are drawn directly from new participant observations and workshop data. No equations, fitted parameters, predictions that reduce to inputs by construction, or load-bearing self-citations appear in the reported chain. The central claims rest on fresh empirical evidence rather than tautological re-derivation of prior results. This matches the default case of a self-contained empirical study without circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The central claim rests on two domain assumptions about method suitability and evaluation validity, with no free parameters or invented entities.

axioms (2)

domain assumption Structured methods from strategic foresight are appropriate bases for AI impact assessment brainstorming.
Paper adapts two such methods in the first contribution without detailed justification of fit to AI contexts.
domain assumption Participant perceptions and observed behaviors in workshops validly indicate real impact assessment quality and brainstorming effectiveness.
Used to evaluate the AI interventions and derive the design guidance.

pith-pipeline@v0.9.0 · 5501 in / 1324 out tokens · 62201 ms · 2026-05-07T07:50:28.126569+00:00 · methodology

discussion (0)

Reference graph

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