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REVIEW 3 major objections 4 minor 128 references

Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking

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

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Pith's one-line read Fact-checkers demand explanations they can replay and verify, interview study finds

desk verdict A careful interview study that gives fact-checkers' stated explanation needs (replicable, verifiable, uncertainty-aware) the field was missing, with the caveat that stated preferences are not demonstrated utility. read the letter →

arxiv 2502.09083 v1 pith:JAWJ7HZB submitted 2025-02-13 cs.HC cs.AIcs.CL

Greta Warren , Irina Shklovski , Isabelle Augenstein This is my paper · ORCID
classification cs.HCcs.AIcs.CL
keywords explainableAIautomatedfact-checkingexplanationrequirementshuman-centredinterviewstudymisinformationuserneeds
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 what explanations automated fact-checking systems must provide to be useful to the people they are built for. Through interviews with ten professional fact-checkers, it argues that a verdict alone, or a confidence score, is not enough: fact-checkers need to be able to reproduce and verify the system's path to the verdict, and to see where evidence is missing or uncertain. The requirements are stated as three principles: explanations must be replicable, verifiable, and capable of expressing uncertainty and information gaps. The paper also maps these needs onto the four stages of fact-checking work (claim detection, evidence retrieval, veracity prediction, and communication), showing that the same tool needs different explanations at different stages.

What carries the argument

The analytical machinery is a qualitative interview study: ten fact-checkers from five continents, recruited through fact-checking networks and analysed with grounded-theory-style open, axial, and selective coding. The organizing frame is the four-step fact-checking pipeline (claim detection, evidence retrieval, veracity prediction, communication of fact-checks), used to locate each participant's tool use and explanation expectations. The load-bearing analytic result is a triad of requirements, namely replicability, verifiability, and the ability to explain uncertainty and information gaps, which the paper derives from participants' descriptions and contrasts with existing explainability techniques such as attention highlights, saliency maps, rules, counterfactuals, and summaries.

What would settle it

A recruitment survey or interview study that includes fact-checkers from additional languages, regions, and organisation types, and finds explanation requirements that conflict with replicability, verifiability, and uncertainty transparency, would weaken the central claim. Alternatively, a controlled study in which systems meeting the three requirements do not improve fact-checkers' verification speed, accuracy, or trust relative to current explanations would falsify the practical value of the requirements.

Watch

Extended reading notes

Core claim

The central claim is that key human-centred requirements for explanation of automated fact-checking systems are that explanations be replicable, verifiable, and able to explain uncertainty and information gaps. Fact-checkers describe 'showing the work' as the core of their craft: their publications list sources, search terms, and reasoning steps so readers can retrace them. The paper finds that automated explanations fail when they offer only a label, a token highlight, or a percentage confidence score, because those do not let a fact-checker check whether the evidence really supports the verdict or where the information is incomplete. The findings also show that explanation needs shift by pipeline stage: low-stakes claim detection needs little explanation, while veracity prediction demands local, checkable evidence links; evidence retrieval requires transparency about which sources were used and why; and communication calls for structured, verdict-dependent explanations that readers could replicate.

Load-bearing premise

The argument rests on ten self-selected, English-speaking fact-checkers whose self-reported needs, after reaching thematic saturation at eight interviews, are taken to stand for professional fact-checkers worldwide.

Editorial extensions

If this is right

  • Automated fact-checking systems should expose the evidence and reasoning path they used, not only the predicted verdict, so fact-checkers can retrace and check it.
  • Confidence scores should be accompanied by source-level explanations of why the system is uncertain, such as 'this report's author has had papers retracted', rather than a bare percentage.
  • Tools that rely on secondary sources like news articles or Wikipedia will face resistance, because fact-checkers deliberately seek primary sources; systems must show how they judged source relevance and reliability.
  • Explanations must be local and specific, using timestamps in video, frames showing manipulation artefacts, or excerpts of documents, so fact-checkers can point readers to the exact evidence.
  • Different pipeline stages need different explanation depth; the same explanation format will not suit claim monitoring and verdict decisions.

Reading between the lines

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

  • A testable extension is to build a prototype explanation interface that implements the three requirements and measure whether working fact-checkers verify outputs faster or with fewer errors than with current saliency or summary explanations.
  • The requirements imply that 'faithfulness to the model' and 'understandability to the fact-checker' can conflict; the paper's data can be read as supporting user-centred explanations that intentionally describe the process in human fact-checking terms even when the model's actual computation differs.
  • The language inequity finding suggests that explanation requirements interact strongly with geography: fact-checkers in lower-resourced and non-English settings may need additional transparency about training data languages and accents before any explanation is useful.
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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 semi-structured interview study with ten professional fact-checkers from five continents, analyzing how they assess evidence, decide verdicts, explain their work, and use automated tools, in order to derive explanation requirements for automated fact-checking systems. The authors identify three central requirements: explanations must be replicable, verifiable, and able to communicate uncertainty and information gaps, along with stage-specific needs for claim detection, evidence retrieval, veracity prediction, and communication. The paper also documents gaps between current automated fact-checking methods and practitioners' needs, and offers design recommendations such as using primary sources, going beyond binary verdicts, and 'showing the work.'

Significance. If the requirements are accepted, the paper provides a rare, practitioner-grounded set of explanation criteria for automated fact-checking, which is valuable for NLP and HCI researchers. The study is transparently reported (interview guide and codebook in appendices), uses iterative grounded-theory coding with a saturation check, and supports themes with participant quotes. The main limitation is that the normative 'must' in the central claim rests on self-reported preferences about hypothetical systems rather than observed behavior or efficacy evidence; the paper itself cites prior work suggesting explanations may not benefit journalists.

major comments (3)
  1. [Section 5.1] The central claim that explanations 'must be replicable, verifiable, and able to explain uncertainty and information gaps' is based on what participants said they would want from imagined AI tools (Appendix B, questions 15–17), not on observed use of systems embodying these properties. This is acceptable for requirements elicitation, but the paper should address the tension with its own cited evidence that explanations may not improve journalist performance (Schmitt et al. [100]) and that feature-attribution or example-based explanations had no effect on laypeople (Lim and Perrault [75]). The current phrasing overstates the certainty of the finding; the paper should either soften the normative 'must' to 'were expressed by participating fact-checkers' and explicitly discuss behavioral validation as future work, or provide a stronger argument for why self-reported preferences should be treated as design requirements despite this evidence.
  2. [Section 3.1 and Table 1] The sample of 10 self-selected, English-speaking fact-checkers is used to make general claims about 'fact-checkers' worldwide. The limitations in Section 3.4 acknowledge the sample size and language, but the abstract and Section 5.1 present the requirements as universal ('key human-centred requirements for explanation of automated fact-checking systems must be...'). Given the documented linguistic and regional inequities in tool performance (Section 4.2.2), the paper should make the transferability argument explicit: either narrow the scope of the requirements to the interviewed populations and contexts, or provide a more detailed justification for why this sample represents the broader population of fact-checkers, drawing on prior work [62, 81] in a way that acknowledges its own variance.
  3. [Sections 5.2 and 5.3] The design recommendations ('show the work', 'beyond binary verdicts', 'primary sources') are presented as direct implications of the findings, but the paper does not assess the feasibility or potential negative consequences of these requirements. For example, traceable, fully replicable explanations may impose high cognitive load on fact-checkers (who already report time constraints), and 'explaining the model's training data' may conflict with proprietary or security concerns. A brief discussion of trade-offs, even speculative, would make the recommendations more balanced and usable for system designers.
minor comments (4)
  1. [Section 5.1] Typo: 'must bereplicable' should be 'must be replicable'.
  2. [Section 4.1.2] The text 'Six participants (P2, P2, P6, P7, P9, P10)' lists P2 twice; please check the intended participant IDs and the resulting count.
  3. [Section 4.2.3] The phrase 'the evidence infers the predicted output' should be 'the evidence implies (or supports) the predicted output'; evidence does not infer, though models do.
  4. [Section 3.3] The coding process is described as discussed by all authors, but it would strengthen transparency to state whether any transcripts were independently double-coded or whether an inter-coder reliability check was used, or to explicitly justify single-coder coding with team-based refinement.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central requirements are grounded in interview data, not in fitted inputs or the authors' prior results.

full rationale

This paper makes no formal predictions and fits no parameters. The central claim that fact-checkers' explanation requirements are replicability, verifiability, and the ability to express uncertainty and information gaps is a thematic synthesis of semi-structured interviews with ten fact-checkers (Sections 4 and 5.1, Table 2). The evidence chain runs from interview transcripts through open, axial, and selective coding to themes; it does not reduce to an input by construction. Prior publications by the author team appear only as literature context (e.g., [12,16,37,52,54,80,88,91,114]) and are not used to justify the empirical findings. The interview guide was adapted from external prior work [81], not from the authors' own results. Section 3.4 candidly notes limits (small sample, English-only interviews, self-report), but those are standard qualitative validity limitations, not circularity. The skeptic's concern that stated preferences may not predict behavioral utility is an external-validity or correctness question, not a circular-reasoning question. No self-definitional, fitted-input, self-citation-load-bearing, imported-uniqueness, smuggled-ansatz, or known-result-renaming step was found.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

No free parameters or invented entities are present because the paper is a qualitative interview study. The central claim rests on the domain assumptions listed above: self-report accuracy, saturation, and the pipeline frame. The most fragile of these is the generalizability of a ten-person, English-speaking sample.

assumptions (3)
  • domain assumption Participants' self-reported descriptions of their practices and needs are accurate reflections of their real fact-checking work.
    Section 3.3 and 3.4: the analysis codes interview transcripts; the paper acknowledges self-report bias but relies on this data to derive requirements.
  • domain assumption Thematic saturation after eight interviews is sufficient to establish stable themes for the population of professional fact-checkers.
    Section 3.1 claims theoretical saturation after eight interviews, with two additional confirming interviews; this underpins the generalizability of the findings.
  • domain assumption The four-stage fact-checking pipeline (claim detection, evidence retrieval, verdict decision, communication) is a valid frame for analyzing fact-checking work and explanation needs.
    Section 2.1 and Section 4 organize the findings by this pipeline, following prior work; the study's own contribution is mapped onto this frame.

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Cite this review

Pith. "Pith review of Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking." pith.science (2026). https://pith.science/paper/JAWJ7HZB

@misc{pith2026250209083,
  author       = {Pith},
  title        = {Pith review of: Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/JAWJ7HZB}},
  note         = {Machine review of arXiv:2502.09083}
}
read the original abstract

The pervasiveness of large language models and generative AI in online media has amplified the need for effective automated fact-checking to assist fact-checkers in tackling the increasing volume and sophistication of misinformation. The complex nature of fact-checking demands that automated fact-checking systems provide explanations that enable fact-checkers to scrutinise their outputs. However, it is unclear how these explanations should align with the decision-making and reasoning processes of fact-checkers to be effectively integrated into their workflows. Through semi-structured interviews with fact-checking professionals, we bridge this gap by: (i) providing an account of how fact-checkers assess evidence, make decisions, and explain their processes; (ii) examining how fact-checkers use automated tools in practice; and (iii) identifying fact-checker explanation requirements for automated fact-checking tools. The findings show unmet explanation needs and identify important criteria for replicable fact-checking explanations that trace the model's reasoning path, reference specific evidence, and highlight uncertainty and information gaps.

Figures

Figures reproduced from arXiv: 2502.09083 by the authors.

Figure 1
Figure 1. Description of fact-checkers’ AI tool use and explanation needs, contrasted with what AI methods and explanation [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

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Pith tools

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