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REVIEW 3 major objections 6 minor 2 references

Journalists' Perceptions of Artificial Intelligence and Disinformation Risks

T0 review · 3 major / 6 minor · reviewed 2026-08-05 · deepseek-v4-flash

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Pith's one-line read A survey of 504 Basque Country journalists finds that 89.88% believe artificial intelligence will considerably or significantly increase the risk of disinformation.

desk verdict A solid regional survey whose headline prevalence number is credible for the sample but not fully protected against nonresponse bias. read the letter →

arxiv 2509.01824 v1 pith:YEGSQJC7 submitted 2025-09-01 cs.CY cs.AI

Urko Peña-Alonso , Simón Peña-Fernández , Koldobika Meso-Ayerdi This is my paper · ORCID
classification cs.CYcs.AI
keywords artificialintelligencedisinformationriskperceptionjournalismnewsmediasurveydeepfakesBasqueCountry
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 tries to establish, with quantitative evidence, how journalists in the Basque Country perceive the impact of artificial intelligence on disinformation. Based on a structured survey of 504 journalists conducted in 2024, it reports that 89.88% of respondents believe AI will considerably or significantly increase disinformation risks. The perception is consistent across gender and media type, but statistically significantly stronger among journalists with more professional experience and weaker among those who use AI more frequently. The two most frequently cited risks are difficulty detecting false content and deepfakes (37.9%) and obtaining inaccurate data (33.33%), and about half of those selecting two risks paired these two concerns. The paper matters because understanding professional risk perception is a precondition for designing training, ethical guidelines, and regulation as AI becomes embedded in newsrooms.

What carries the argument

The empirical machinery is an ad hoc structured survey administered to 504 Basque Country journalists between May and June 2024, analyzed with Fisher's exact tests, Cramér's V effect sizes, and correspondence analysis (CA). The decisive analytic device for the risk-hierarchy claim is a 4x4 co-occurrence matrix that records which pairs of risks each respondent selected (up to two), revealing that detection difficulty and data inaccuracy co-occur in over half of all paired responses. The CA biplots and the p-values after Holm/Benjamini-Hochberg corrections carry the statistical argument that experience and usage are associated with risk perception.

What would settle it

A random probability-based survey of Basque journalists—using a complete employment list with quotas rather than voluntary response—that finds either a substantially lower majority (e.g., below 75%) reporting AI will considerably/significantly increase disinformation, or no monotonic increase in that perception with years of experience, would directly contradict the paper's central claims.

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Extended reading notes

Core claim

On its own terms, the paper's discovery is that a large majority of working journalists (89.88%, n=453 of 504) classify the impact of AI on disinformation as 'considerably' or 'significantly' increasing, with only 10.12% choosing 'minimally' or 'not at all.' Fisher's exact tests show no significant association with gender or media type, but a significant positive association with years of professional experience (Cramér's V = 0.144, small-to-medium) and a significant negative association with frequency of AI use (Cramér's V = 0.120): heavy users perceive less risk. Correspondence analysis supports both patterns, with experience aligned toward high-risk categories and non-users aligned toward

Load-bearing premise

The paper assumes that the 504 journalists who voluntarily responded through official directories and a professional association represent all ~5,000 journalists in the Basque Country; if self-selection attracted those with stronger views on AI, the 89.88% figure could be inflated.

Editorial extensions

If this is right

  • If nearly nine in ten journalists are right to fear AI-driven disinformation, newsrooms face a pressing need for deepfake detection tools and verification protocols tailored to the profession's stated top risk.
  • The significant positive association between years of experience and risk perception implies that veteran journalists may be the most cautious voices; training and change-management efforts may need to address their specific concerns.
  • The significant negative association between AI use and perceived risk suggests that hands-on familiarity with AI tools may make journalists less alarmed, potentially a moderating force as adoption grows.
  • The co-occurrence of detection difficulty with data inaccuracy indicates that solutions should treat verification and data quality as a single integrated problem rather than separate issues.
  • The absence of significant gender or media-type differences suggests a broadly shared professional consensus, which could support collective professional norms or policy positions on AI governance.

Reading between the lines

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

  • If the usage-risk relationship is causal (familiarity reduces alarm), then the perceived disinformation threat may decline as AI becomes standard practice; a longitudinal replication could test whether the 89.88% figure drops over time.
  • Because the survey used official directories and a professional association for recruitment, the sample may over-represent established professionals; an independent probability sample could reveal whether the experience gradient is a genuine generational effect or a recruitment artifact.
  • The Basque Country's distinctive multilingual media landscape may shape results; extending the same instrument to other peripheral or multilingual regions would clarify whether the observed consensus is regional or general.
  • The co-occurrence pattern suggests a testable extension: experimentally providing journalists with deepfake-detection training should, if the perceived linkage holds, also reduce their concern about inaccurate data.
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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 / 6 minor

Summary. The paper reports a quantitative survey of 504 journalists in the Basque Country, conducted in May–June 2024, about their perceptions of artificial intelligence (AI) and disinformation risks. The main descriptive finding is that 89.88% of respondents believe AI will 'considerably' or 'significantly' increase disinformation risks. The authors also examine associations between perceived risk and gender, years of professional experience, type of media, and respondents' use of AI; they report significant associations with experience and AI use. A further section ranks perceived risk categories and analyzes co-occurrences of risk pairs. The manuscript concludes that journalists view AI as a serious threat to information integrity, with relevant differences by experience and technology familiarity, and discusses implications for training and AI literacy.

Significance. If interpreted as a description of the surveyed sample, the paper provides useful, localized evidence about journalistic risk perceptions in a region with a distinct media landscape. Strengths include the use of appropriate bivariate statistical tests with effect sizes, adjustment for multiple comparisons, correspondence analysis for visualization, and a limitations section that acknowledges self-selection. The paper's central claim, however, is a population prevalence estimate for approximately 5,000 Basque journalists, and that inference is not supported by the reported recruitment and response information. The co-occurrence analysis, though secondary, also contains reporting inconsistencies. The topic is timely and the dataset could be a valuable contribution, but the manuscript needs to either supply more sampling information or substantially qualify its prevalence claims.

major comments (3)
  1. [Sections 2 and 3.1] The headline result—89.88% of journalists believing AI will considerably or significantly increase disinformation risk—is presented as a population prevalence (e.g., 'The majority of professionals perceive...'). However, the Methods do not report the number of journalists invited or the response rate; recruitment is described only as identification through the Open Communication Guide and collaboration with the Basque Association of Journalists. The Limitations section itself acknowledges that voluntary participation may attract individuals with stronger views. The reported ±4.15% margin of error is a simple-random-sampling formula and does not quantify nonresponse or self-selection bias. Because the central claim is a prevalence estimate, this is load-bearing: a modest difference between respondents and non-respondents could materially move the 89.88% figure. Please report the invitatio
  2. [Section 3.2, Table 2] The co-occurrence table contains internal inconsistencies that undermine the quantitative summary of RQ3. The counts listed sum to 501, not 504, despite the note stating they are based on 504 respondents. Several percentages do not match the counts: 17/504 = 3.37%, not 3.99%; 69/504 = 13.69%, not 13.77%. The text states 'Among the journalists who selected two risks, 50.5% (n = 253) ...', but 253/504 = 50.2%, and the denominator of 'journalists who selected two risks' is not reported. Please clarify how respondents who selected only one risk, or only 'none other', are handled, and recompute all percentages with a precisely defined denominator. The top-pair ranking is likely unaffected, but the current presentation is not reproducible.
  3. [Section 4 (Discussion)] The discussion suggests that 'direct experience and familiarity with AI may foster a more nuanced understanding of both its potential and its limitations.' This causal interpretation goes beyond the cross-sectional association reported in Section 3.1. The inverse relationship between AI use and risk perception is equally compatible with selection effects (journalists who perceive lower risk are more likely to adopt AI) or with unmeasured confounding (age, digital skills, newsroom resources). Please temper the causal language and explicitly acknowledge the direction-of-causation problem. This issue is load-bearing for the secondary claim about AI use and risk perception.
minor comments (6)
  1. [Introduction] Typo: 'inr oads' should be 'inroads'.
  2. [Author affiliations] The corresponding author email is given as 'urko.pena@ehu.eus@ehu.eus'; the duplicate domain is likely a typo.
  3. [Section 3.1] The binary grouping of 'considerably' and 'significantly' into a high-risk category is used for the headline 89.88%. Please clarify whether this aggregation was planned a priori or decided after data collection, and consider noting that the scale anchor 'significantly' may be read ambiguously by some readers.
  4. [Figure 1 and Methods] The text says 'The heatmaps presented in this study were generated using ChatGPT 4.1', but the figures appear to be percentage distributions and correspondence-analysis plots, not heatmaps. Please correct or clarify this statement.
  5. [References] The citation 'Peña-Fernández et al. (2023)' is used for two different works (Peña Fernández, Meso Ayerdi, Larrondo Ureta, & Díaz Noci; and Peña-Fernández, Peña-Alonso, & Eizmendi-Iraola). Please disambiguate.
  6. [References and Methods] Some online references have no access date (e.g., Basque Government Open Communication Guide, Brundage et al. 2018). The Methods should state when the survey link was active and when the frame was last accessed.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity; empirical survey reports self-reported perceptions directly, with self-citations only as context.

full rationale

This paper is an empirical survey, not a derivation. The central claim—that 89.88% of 504 Basque journalists believe AI will increase disinformation risks—is a direct summary of the respondents' own answers to a perception question, not a quantity derived from fitted parameters, a model, or the authors' prior work. The statistical tests (Fisher's exact test, Cramér's V, correspondence analysis) take the survey responses as inputs and report associations; no output is fed back into the analysis in a way that would make the conclusion equivalent to its inputs by construction. The paper does cite prior work by the same research group (e.g., Peña-Fernández et al., 2023), but these citations are used for contextual framing in the Introduction and Discussion, not as load-bearing evidence for the empirical findings. The acknowledged limitation of voluntary participation and possible self-selection is a real threat to external validity, but it is a bias/validity concern, not circular reasoning. Under the specified criteria, there is no step where a prediction reduces to a fitted input or where the central claim depends on a self-citation chain.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

The central claim rests on survey methodology: a representative sample, reliable self-reports, and standard statistical assumptions. No free parameters or invented entities are involved.

assumptions (4)
  • domain assumption The 504 surveyed journalists are representative of the Basque journalist population.
    The sample is drawn from official directories and voluntary participation; self-selection may bias results, as acknowledged in the limitations section.
  • domain assumption The estimated journalist population in the Basque Country is approximately 5,000.
    Used to compute margin of error (±4.15%). If the population estimate is wrong, the margin of error changes, but the central percentage is unaffected.
  • domain assumption Survey responses accurately reflect respondents' perceptions.
    The study relies on self-reported attitudes; no validation against behavior or external benchmarks.
  • standard math Statistical test assumptions after category grouping are met.
    Chi-square and Fisher exact tests assume independent observations and adequate cell sizes; the authors regroup categories to meet this, potentially altering results.

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Cite this review

Pith. "Pith review of Journalists' Perceptions of Artificial Intelligence and Disinformation Risks." pith.science (2026). https://pith.science/paper/YEGSQJC7

@misc{pith2026250901824,
  author       = {Pith},
  title        = {Pith review of: Journalists' Perceptions of Artificial Intelligence and Disinformation Risks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YEGSQJC7}},
  note         = {Machine review of arXiv:2509.01824}
}
read the original abstract

This study examines journalists' perceptions of the impact of artificial intelligence (AI) on disinformation, a growing concern in journalism due to the rapid expansion of generative AI and its influence on news production and media organizations. Using a quantitative approach, a structured survey was administered to 504 journalists in the Basque Country, identified through official media directories and with the support of the Basque Association of Journalists. This survey, conducted online and via telephone between May and June 2024, included questions on sociodemographic and professional variables, as well as attitudes toward AI's impact on journalism. The results indicate that a large majority of journalists (89.88%) believe AI will considerably or significantly increase the risks of disinformation, and this perception is consistent across genders and media types, but more pronounced among those with greater professional experience. Statistical analyses reveal a significant association between years of experience and perceived risk, and between AI use and risk perception. The main risks identified are the difficulty in detecting false content and deepfakes, and the risk of obtaining inaccurate or erroneous data. Co-occurrence analysis shows that these risks are often perceived as interconnected. These findings highlight the complex and multifaceted concerns of journalists regarding AI's role in the information ecosystem.

Figures

Figures reproduced from arXiv: 2509.01824 by the authors.

Figure 1
Figure 1. Percentage distribution of perceived disinformation risk by media type (X-axis: percep￾tion attributes; Y-axis: media type). Source: Author’s own work 3. Results 3.1. Journalists’ Perceptions of Disinformation Associated with AI The majority of professionals perceive that artificial intelligence will significantly increase the risks of disinformation. Specifically, 89.88% of the responses fall into the categories “c… view at source ↗
Figure 2
Figure 2. Percentage distribution of perceived disinformation risk by gender (X [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Percentage distribution of perceived disinformation risk [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 5
Figure 5. Figure 5: Correspondence analysis of artificial intelligence usage in the workplace and journalists’ perceptions of disinformation risk. Source: Author’s own work [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

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Reference graph

Works this paper leans on

2 extracted references · 2 linked inside Pith

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    periodismo de verdad

    https://doi.org/10.3145/epi.2023.sep.15. Cui, J. (2025). Digital transformation in the media industry: The moderating role of human- AI interaction technologies. Media, Communication, and Technology, 1(1), 42– 47. https://doi.org/10.30560/mct.v1n1p42. D’Andrea, A., Fusacchia, G., & D’Ulizia, A. (2025). Linguistic insights, media mechanisms, and the role o...

    arXiv 2023

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    I Resist

    https://doi.org/10.31921/doxacom.n38a2029. Morosoli, S., Resendez, V., Naudts, L., Helberger, N., & de Vreese, C. (2025). “I Resist.” A study of individual attitudes towards generative AI in journalism and acts of resistance, risk perceptions, trust, and credibility. Digital Journalism, 1 –20. https://doi.org/10.1080/21670811.2024.2435579. Murcia Verdú, F...

    arXiv 2025

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Reviewed August 5, 2026 · model on record in the stance chip above.