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REVIEW 3 major objections 4 minor 1 cited by

Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society

T0 review · 3 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read Computing research uses future scenarios for five recurring objectives, from gathering stakeholder needs to anticipating threats, and mostly at a consultation level of public participation.

desk verdict A transparent and useful map of scenario methods in ACM computing papers, but the keyword query misses sub-methods the paper itself names, so the headline distributions should be read as ACM-illustrative, not definitive. read the letter →

arxiv 2506.05605 v1 pith:TO4BJL36 submitted 2025-06-05 cs.HC cs.AI

Julia Barnett , Kimon Kieslich , Jasmine Sinchai , Nicholas Diakopoulos This is my paper
classification cs.HCcs.AI
keywords scenariobuildingsystematicliteraturereviewparticipatorydesignspeculativetechnologyfuturesanticipatorygovernancestakeholderparticipationemerging
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

Scenario building—writing short narratives about possible futures—has spread from military planning and business strategy into computing research, but nobody had mapped what computing researchers actually use it for. This paper reports a systematic review of 59 full research articles from one major computing research catalog, published between 2015 and 2025, that use scenarios to explore future impacts of emerging technologies. It claims the field's scenario work clusters into five objectives: gathering stakeholder needs and values, empowering marginalized groups to imagine technology futures, provoking ethical reflection, anticipating threats and risks, and exploring perceptions of novel technologies before they launch. It also claims the participatory element is real but shallow: most studies consult stakeholders on expert-written scenarios, while only five studies let participants own the process. If the taxonomy holds, researchers can use it as a shared vocabulary and as a checklist of objectives the field has not yet taken up.

What carries the argument

Two instruments carry the argument. The first is an inductively built taxonomy of five objectives, coded from the researchers' own statements of purpose in each paper; this is what grounds the claim that scenario work clusters into needs-gathering, marginal-group empowerment, ethical reflection, threat anticipation, and novel-technology perception. The second is a four-level participation ladder—consult, include, collaborate, own—used to classify how stakeholders relate to the scenarios. The ladder does the work for the paper's inclusivity finding: the distribution across levels shows consultation-dominant practice and near-absence of participant ownership. A systematic screening pipeline, reported following structured-review guidelines, is what turns 555 candidate records into a defensible 59-paper corpus.

What would settle it

Rerun the same search and inclusion rules across the social-science, futures, ethics, philosophy, legal, and non-English venues the paper identifies, then compare the objective taxonomy and participation levels. If the distribution shifts materially—for instance, if policy- and regulation-oriented objectives emerge as a major category, or if consultation is no longer the most common participation level—the claim that these are the five main uses of scenarios in computing research would need to be revised.

Watch

Extended reading notes

Core claim

The paper's central claim is that scenario building has become a recognizable method in computing research with a small set of recurring purposes. Across the 59-paper corpus, the most common stated objective is to gather stakeholder needs and values (22 of 59), followed by empowering marginalized groups to imagine technology futures (12 of 59), provoking ethical reflection and critical awareness (10 of 59), anticipating threats and risks (10 of 59), and exploring perceptions and impacts of new technologies (5 of 59). The paper further claims that most scenario research is participatory in a limited sense: 21 of 59 papers operate at the consultation level, where participants respond to researcher-written scenarios, whereas only 5 of 59 reach the ownership level, where non-experts create the scenarios and hold outcome authority. Non-experts played a crucial role in either creation or evaluation in 64% of the corpus. The conclusion is that scenarios in computing are predominantly user-centered rather than citizen-empowering, and that higher-ownership participatory scenario work remains rare.

Load-bearing premise

The load-bearing premise is that papers in one English-language computing research catalog, found through the chosen scenario-related search terms, represent computing research as a whole; if that corpus is not representative, the five-category taxonomy and participation distribution could shift.

Editorial extensions

If this is right

  • Computing researchers can use the five objectives as a design menu when planning scenario studies, and the shared vocabulary can help connect fragmented terms like speculative design, scenario-based design, and scenario writing.
  • If scenario work is meant to be inclusive, the field has room to move: only 5 of 59 studies reached the ownership participation level, so higher-ownership designs are an open opportunity.
  • Because needs-gathering is the most common objective but tends to be paired with consultation-level participation, there is a direct path for future work to combine these goals with collaborate- or own-level methods.
  • The recent concentration of studies from 2022 through 2024, mostly on AI, suggests scenario building is becoming a standard anticipatory tool for AI governance and risk work.
  • Non-experts already play a crucial role in 64% of the corpus, which strengthens the argument that lay stakeholders can meaningfully contribute to technology assessment when given narrative materials.

Reading between the lines

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

  • Extending beyond the paper: broadening the corpus to social-science, futures, ethics, philosophy, legal, and non-English venues would likely surface a sixth objective—shaping regulation and policy—since the paper's own references show such work exists in those fields.
  • Extending beyond the paper: the two LLM-written-scenario papers suggest a scalable test—if LLMs can generate and vary scenarios cheaply, the bottleneck for participatory work may shift from scenario creation to evaluation, making consultation-level studies even more common rather than more empowering.
  • Extending beyond the paper: applying the same coding scheme to the next five years of publications would test whether the 2022-2024 surge marks a lasting methodological turn or a temporary wave tied to generative AI interest.
  • Extending beyond the paper: one could build a practical checklist from the taxonomy and ladder and use it in workshops with researchers, observing whether naming the ownership level changes how they design future scenario studies.
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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 / 4 minor

Summary. This paper reports a systematic literature review of research articles in the ACM Digital Library published between January 2015 and January 2025 that use scenario building to explore future developments or impacts of emerging technologies. After screening 555 records, the authors analyze 59 full papers and inductively derive five objectives for using scenarios: gathering stakeholder needs and values (37%), empowering marginalized groups (20%), provoking ethical reflection (17%), anticipating threats and risks (17%), and exploring perceptions of novel technologies (8%). They also apply Delgado et al.'s four-level participation ladder and report that most studies involve consultation-level participation, with higher-ownership participation rare, and that non-experts played a role in creation or evaluation in 64% of the corpus. The paper also provides descriptive statistics on technology focus, terminology, and scenario medium.

Significance. The paper addresses a real gap: the fragmented terminology around scenario methods in computing, and it offers a usable taxonomy plus a participation-level analysis that could help researchers position their own scenario work. The PRISMA-style reporting, the high Cohen's Kappa for abstract screening (0.94), the publicly shared coding documents, and the detailed appendix tables are strengths. If the corpus were demonstrably representative, the five-category taxonomy and the finding that consultation dominates would be a useful contribution to the HCI and anticipatory governance communities. However, the contribution is currently scoped more narrowly than the title and research questions promise, because of the search-query and corpus-construction issues discussed below.

major comments (3)
  1. [Section 3.1, keyword search] The finalized ACM DL query does not include 'science fiction prototyping', 'design fiction', 'storytelling', or 'future workshop', yet Section 2.1 explicitly identifies 'science fiction prototyping' and 'storytelling' as sub-categories of scenario building, and 'design fiction' (Ringfort-Felner et al. 2023) appears in the corpus itself. Because the query is restricted to title/abstract and these terms are absent, the corpus systematically under-represents an entire branch of the method. This is load-bearing: the percentages in Sections 4.1 and 4.2, the participation-level counts, and the 64% figure in Section 5 are all computed from this corpus, so recall loss can change the distribution of objectives and participation levels, not just the denominator. The manual supplement of four AIES '24 records does not correct for this. Please either expand the query to include these named sub-methods and re-run the search, or explicitly reframe the paper's contribution as scoped to the set of terms queried rather than to 'scenario building' as defined in Section 2.1.
  2. [Appendix A, Table 1 (Hosseini et al. 2015)] This row describes a discrete event simulation of an oncology department, which falls under the simulation/variable-setting category that Section 3.1 explicitly excludes ('scenario-based designs such as a predetermined set of variables or characteristics... for the purposes of testing; not futuring scenarios'). Its inclusion as a 'What-If Scenario' suggests that the full-text screening criteria were applied inconsistently. Because the paper does not report inter-rater reliability for full-text eligibility or for the qualitative coding of objectives and participation levels (Section 3.4), it is difficult to assess how widespread such miscodings might be. Please re-audit all 59 included papers against the stated inclusion/exclusion criteria and report reliability statistics for the full-text stage.
  3. [Section 4.2 and Section 5, the 64% claim] The paper counts 'expert-written scenarios with non-expert evaluation' (17% of the corpus) as a case in which 'non-experts played a crucial role in either creation or evaluation.' Under the four-level participation ladder, such evaluation-only studies fall under Level 1 'Consult,' which the paper itself describes as the least involved form of participation. Lumping evaluation-only consultation together with co-creation and ownership overstates the degree of participation. At minimum, the 64% figure should be decomposed by participation level, and the claim that evaluation constitutes a 'crucial role' should be justified or softened.
minor comments (4)
  1. [Table 4] The row for Lamparth et al. contains the typo 'Consutl' instead of 'Consult.'
  2. [Table 2] The row for Almohamed et al. uses 'Collaboration' while all other rows in the participation column use 'Collaborate'; the terminology should be consistent.
  3. [Figure 3] The caption does not explain the denominator (56 papers) or why three corpus papers are excluded; please clarify in the caption or text.
  4. [Section 5.1] The limitation paragraph mentions that 167 extended abstracts were excluded but does not estimate how many of those would have met the other inclusion criteria; a brief sensitivity-oriented discussion of how this exclusion might affect the trends in Figure 2 would strengthen the manuscript.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the systematic review's taxonomy and participation percentages are inductively derived from an externally selected corpus, and the self-citations are illustrative rather than load-bearing.

full rationale

The paper's central claims are a five-category taxonomy of scenario-building objectives and a quantified distribution of participatory levels, both computed from a 59-paper corpus assembled via a PRISMA-guided ACM Digital Library keyword search. There is no mathematical derivation, fitted parameter, or predictive model whose output reduces to its input. The search strategy relies on external sources (e.g., the scenario-planning review by Amer, Daim, and Jetter; PRISMA; the participation framework of Delgado et al.), and the screening process used Cohen's kappa and full-text eligibility assessment. The objective categories and participation levels were developed through inductive coding of the included papers, which is the standard, non-circular process for a systematic review. The self-citations (Barnett, Kieslich, and Diakopoulos 2024; Barnett et al. 2025; Kieslich, Helberger, and Diakopoulos 2024, 2025; Diakopoulos and Johnson 2021) appear as examples of scenario use or as related prior work, but the aggregate distributions do not depend on these papers as evidence; removing them would not change the taxonomy or the participation framework. The acknowledged limitations (ACM-only, English-only, keyword-recall gaps such as science fiction prototyping and storytelling not in the finalized query) are validity and completeness concerns, not circularity: they affect recall and generalizability but do not make the conclusions equivalent to the inputs by construction. Overall, no load-bearing circular step was identified.

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

No numerical model is fit and no new entities are postulated. The central quantitative outputs are counts from manual coding, so the main burdens are scope assumptions and coding reliability rather than fitted parameters.

assumptions (5)
  • domain assumption The ACM Digital Library is treated as an adequate proxy for 'computing research'.
    The entire search is scoped to ACM DL (Section 3.1); the paper later acknowledges in Section 5.1 that other venues are excluded, yet the central generalizations about the field rest on this scope.
  • domain assumption The keyword set captures the fragmented vocabulary of scenario methods.
    The search relies on terms such as 'scenario*', 'speculative*', and 'what-if*'; missing terms such as 'design fiction' or 'future scenarios' could omit relevant publications (Section 3.1).
  • domain assumption The Rotolo et al. definition of emerging technology, applied liberally, is a valid inclusion filter.
    The authors apply the definition liberally and err on the side of inclusion (Section 3.1); different application of this filter would change the 59-paper corpus.
  • domain assumption The four-level participation scale from Delgado et al. (2023) is a valid coding framework.
    The scale is used to classify every paper's participatory level (Section 4.2), so the participation counts depend on this externally defined framework.
  • domain assumption Qualitative coding by the authors is sufficiently reliable.
    Only abstract screening reports Cohen's Kappa (0.94, Section 3.2); full-text coding of objectives and participation is not inter-rater validated, yet it produces the central numeric claims.

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Cite this review

Pith. "Pith review of Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society." pith.science (2026). https://pith.science/paper/TO4BJL36

@misc{pith2026250605605,
  author       = {Pith},
  title        = {Pith review of: Scenarios in Computing Research: A Systematic Review of the Use of Scenario Methods for Exploring the Future of Computing Technologies in Society},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TO4BJL36}},
  note         = {Machine review of arXiv:2506.05605}
}
read the original abstract

Scenario building is an established method to anticipate the future of emerging technologies. Its primary goal is to use narratives to map future trajectories of technology development and sociotechnical adoption. Following this process, risks and benefits can be identified early on, and strategies can be developed that strive for desirable futures. In recent years, computer science has adopted this method and applied it to various technologies, including Artificial Intelligence (AI). Because computing technologies play such an important role in shaping modern societies, it is worth exploring how scenarios are being used as an anticipatory tool in the field -- and what possible traditional uses of scenarios are not yet covered but have the potential to enrich the field. We address this gap by conducting a systematic literature review on the use of scenario building methods in computer science over the last decade (n = 59). We guide the review along two main questions. First, we aim to uncover how scenarios are used in computing literature, focusing especially on the rationale for why scenarios are used. Second, in following the potential of scenario building to enhance inclusivity in research, we dive deeper into the participatory element of the existing scenario building literature in computer science.

Figures

Figures reproduced from arXiv: 2506.05605 by the authors.

Figure 1
Figure 1. PRISMA flow diagram detailing the corpus of [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Stacked bar plot displaying the count of papers [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Stacked bar plot of the 56 papers in our corpus with [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Informing AI Policy Assessment using Large-Scale Simulation of Interventions

    cs.CY 2026-04 conditional novelty 6.5 of 10

    A genetic algorithm optimizes weighted combinations of LLM-perceived harm mitigation, expert costs, and participatory scores over stakeholder-action pairs to surface viable AI policy packages for media harms.

Reference graph

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Pith tools

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