REVIEW 3 major objections 6 minor 2 references
Artificial Intelligence Training in Media: Addressing Technical and Ethical Challenges for Journalists and Media Professionals
T0 review · 3 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash
Pith's one-line read Basque media professionals are largely untrained in AI, and the gap is sharpest inside newsrooms.
desk verdict A genuinely new regional dataset on AI training for media workers, but the headline 14.1% and the subgroup comparisons outrun what the self-selected sample can support. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The analytical engine is a three-way crosstabulation of self-reported AI training by type of media (press, radio, television, digital, press office, advertising agency), broadcasting scope (local, regional, national, international), and job role (editor, presenter, technical, management, and others), computed from the 504 survey responses and interpreted through six semi-structured interviews with innovation and technology leaders. The main organising distinction is between core journalistic tasks and technical, managerial, or persuasive roles: the closer a role is to traditional journalism, the lower the training rate. A second distinction, between employer-provided and self-directed training, shows that company programmes reach only a small minority and mostly those already motivated to learn.
What would settle it
A stratified random sample (or full census) of Basque media workers drawn from employer payrolls rather than voluntary online lists that finds the trained share materially higher than 14.1%, or company training logs showing that more than 8.5% of employees have completed employer-provided AI training, would overturn the paper's headline gap.
Extended reading notes
Core claim
The central discovery is an inverse relationship between AI training and proximity to core journalistic work. Editors (13.8%) and presenters (6.3%) show the lowest training rates, while technical staff (66.7%), management (25.0%), public relations (24.0%), and advertising (44.4%) show the highest; only 8.5% of all respondents received employer-provided training, compared with 12.9% who trained on their own. Internationally based outlets report nearly double the training rate of local outlets (21.7% vs. 12.2%), and digital-native outlets (25.8%) far outpace television (5.8%), radio (10.8%), and print (9.6%). The authors read this as evidence that AI adoption will deepen the digital divide between large and small outlets unless training becomes systematic and includes ethical as well as technical content.
Load-bearing premise
The survey sample is assumed to stand in for the roughly 5,000 media workers in the Basque Country, but respondents were recruited through an open government listing and a professional association without a documented response rate, so a self-selection bias toward digitally active or AI-curious workers could make the 14.1% trained figure an overestimate.
Editorial extensions
If this is right
- As AI tools enter editing and presenting workflows, the people making publication decisions will be the least trained to detect errors, bias, or fabrication, so the risk of unchecked AI output rises.
- Digital-native and international outlets will pull further ahead of local and traditional media, widening a technology gap that already exists from earlier digitalisation.
- Because only 8.5% of respondents had employer-provided training, current workplace policies alone are unlikely to close the gap.
- Ethics-focused AI training needs to be embedded in formal journalism education, not left to self-study, which has reached only a small minority.
Reading between the lines
- If the role gradient is not specific to the Basque market, similar newsroom-versus-technical divides should appear in other mid-sized media markets; a multi-region replication survey would test this directly.
- The dominance of self-directed learning suggests a selection effect, so simply adding courses may not reach editors and presenters unless training is embedded in daily workflows.
- The international-outlet advantage could reflect either more resources or greater exposure to AI-driven platforms; distinguishing those causes would change where policy should intervene.
- Company training records, rather than self-reports, could independently verify whether the 8.5% employer-provided-training figure understates actual provision.
Signed reviews
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. This mixed-method study surveys 504 media professionals in the Basque Country and interviews six media-technology leaders to assess the current state of AI training in regional newsrooms. The central descriptive finding is that only 14.1% of respondents have received any AI training, with most of that training self-directed. The paper further reports that AI training is more common among professionals in digital, advertising, technical, and managerial roles than among editors and presenters, and more common in international and larger organizations than in local outlets. Qualitative interviews are used to contextualize these patterns, highlighting the need for continuous, ethically grounded training. The authors conclude that urgent investment in AI literacy is required, especially for traditional newsroom staff and local media.
Significance. If the descriptive estimates were representative of the roughly 5,000 media professionals in the Basque Country, this would be a valuable, policy-relevant contribution to an understudied regional media system. The paper's strengths include a comparatively large sample for a regional study, detailed disaggregation by media type, role, experience, and scope, transparent reporting of raw cell counts that lets readers judge small-cell fragility, and qualitative triangulation through interviews with innovation leaders. The main limitation is that the survey uses a nonprobability online panel with no response rate or representativeness check, so the headline figure and subgroup gaps should be treated as sample statistics rather than population estimates. The absence of inferential statistics (confidence intervals or significance tests) further weakens the comparative claims. As an exploratory study the paper is useful, but the framing overstates the evidentiary basis.
major comments (3)
- [Section 3, Materials and Methods] The reported margin of error of ±4.15% is justified only under simple random sampling from a target population of about 5,000, but participants were recruited through an online panel advertised via the Open Communication Guide and the Basque Journalists' Association, with no response rate, invitation count, or comparison to the Basque Government census used to define the population. This margin of error does not quantify the uncertainty around the 14.1% headline figure under self-selection bias, which could shift the estimate substantially. Please either provide demographic or other evidence that the sample is representative of the ~5,000 media workers, or explicitly reframe the results as describing the 504 respondents only and remove the margin-of-error claim.
- [Section 4.1, Tables 1 and 2] The paper repeatedly claims 'significant differences' (e.g., 'Significant differences in AI training are observed across different types of media' and 'international outlets reporting nearly double the training rate (21.7%) of local outlets (12.2%)') without any significance tests, confidence intervals, or effect sizes. For the international-vs-local comparison, the approximate standard error of the difference is about 9.3 percentage points, so the observed 9.5-point gap is within sampling noise; a similar problem affects several small cells, such as technical workers (2 of 3 trained) and presenters (6 trained in total). The authors should either add appropriate inferential statistics (including confidence intervals for proportions) or soften all comparative claims to avoid overstating differences that the sample cannot support.
- [Section 4.1, Table 2 and discussion of leadership roles] The claim that leadership-role holders receive 'twice as much training' (19.9% vs. 9.4%) and the subsequent inference that 'employees with longer tenures' are more likely to be trained are based on bivariate cross-tabulations only. Tenure, responsibility role, age, and media type are correlated, so the independent contribution of each cannot be assessed without a multivariate model or at least stratified analysis. Please add such an analysis or explicitly caveat that these are unadjusted associations and avoid causal phrasing such as 'likely factors influencing this trend.'
minor comments (6)
- [Section 4.2] The text states that among trained respondents, '52.1% did so through both personal and company efforts, 39.4% relied solely on self-directed learning, and only 8.5% received training exclusively from their employer,' but these percentages cannot be directly derived from Table 5, which reports percentages of all respondents rather than of the 71 trained participants. Please reconcile the text and table by stating the denominator explicitly and, if possible, adding a row for 'any training' with mutually exclusive categories.
- [Section 4.2] The text refers to 'Table 4' in the sentence 'This trend aligns with the types of roles that reported higher training levels (Table 4),' but no Table 4 appears; the following table is numbered Table 5. Please renumber the tables sequentially.
- [Tables 1-3 and 5] Decimal separators are used inconsistently: some percentages use commas (e.g., '12,2') and others use periods (e.g., '6.3'). Please standardize throughout, especially within table cells.
- [Section 4.1] The survey instrument's 'self-study' category is undefined; specify whether it includes reading books, online tutorials, webinars, or other informal learning formats, since this affects interpretation of the self-directed learning finding.
- [Section 3, Interviews] The selection criteria for the six interviewed media leaders are not explained beyond their being 'senior figures' at 'prominent' outlets. Please state how these outlets span the regional media landscape (e.g., ownership, language, reach, medium) and whether saturation was considered in the analysis.
- [Section 5, Discussion] The paper lacks a dedicated limitations subsection. In addition to the sampling and inference issues raised above, the data were collected in May-June 2024, a period of extremely rapid AI tool development; the authors should acknowledge that the reported training rates may already be outdated and that the situation is likely evolving.
Circularity Check
No circularity: the paper is an empirical survey whose findings are direct tabulations of responses, not derived from its inputs or self-citations.
full rationale
This is an empirical survey with no fitted model, mathematical derivation, or predictive chain that could reduce to its own inputs. The central finding (14.1% of professionals have received AI training) is a direct tabulation of the 504 survey responses, and the subgroup comparisons are straightforward cross-tabulations reported in Tables 1-5. The self-citations by the authors (e.g., Peña-Fernández et al. 2023a; Peña-Fernández et al. 2023b; Díaz-Noci et al. 2024; Larrondo-Ureta and Peña-Fernández 2024) appear only in the literature review and discussion as contextual or interpretive support, and none is load-bearing for the empirical results. The ±4.15% margin of error is a standard sample-size calculation based on an assumed population of 5,000; whether the nonprobability sampling frame supports generalization is an external-validity concern, not a circularity concern. No claim in the paper is justified by an author-defined construct, a fitted parameter renamed as a prediction, or a uniqueness theorem imported from the authors' prior work. Therefore the derivation chain, such as it is, is self-contained and non-circular.
Assumptions & free parameters
assumptions (3)
- domain assumption Survey respondents are representative of the approximately 5,000 media professionals in the Basque Country.
- domain assumption Self-reported AI training accurately reflects actual completed training.
- domain assumption Six innovation leaders' views adequately represent media organizations' training policies.
Cite this review
Pith. "Pith review of Artificial Intelligence Training in Media: Addressing Technical and Ethical Challenges for Journalists and Media Professionals." pith.science (2026). https://pith.science/paper/PVCU2YIF
@misc{pith2026250820137,
author = {Pith},
title = {Pith review of: Artificial Intelligence Training in Media: Addressing Technical and Ethical Challenges for Journalists and Media Professionals},
year = {2026},
howpublished = {\url{https://pith.science/paper/PVCU2YIF}},
note = {Machine review of arXiv:2508.20137}
}
read the original abstract
The rise of Artificial Intelligence (AI) is presenting both technical and ethical challenges for media organisations, creating an urgent need for professional training. This study explores how media professionals in the Basque Country are equipping themselves to face these challenges. Using a mixed-method approach, it combines a survey of 504 active professionals with in-depth interviews with six innovation leaders from major regional media outlets. The findings reveal that only 14.1% of professionals have undergone AI training, mostly through self-learning. Larger, internationally focused companies are more proactive in providing training, while local and traditional media organisations show significant gaps. Technical and managerial roles are leading the way in adopting AI, whereas newsroom staff are notably behind. The study highlights the pressing need to enhance AI training, with a particular focus on ethical and technical aspects, both through in-house programmes and formal education pathways.
Reference graph
Works this paper leans on
-
[26]
Use of chatbots for news verification Communication and applied technologies
IEEE. Alonso González, M., and Sánchez Gonzales, M. (2024). Inteligencia artificial en la verificación de la información política: Herramientas y tipología. Más Poder Local 56, 27 –45. doi: 10.56151/maspoderlocal.215 Arias Jiménez, B., Rodríguez -Hidalgo, C., Mier -Sanmartín, C., and Coronel -Salas, G. (2022). “Use of chatbots for news verification Commun...
arXiv 2024
-
[625]
Bias, journalistic endeavours, and the risks of artificial intelligence
doi: 10.62345/jads.2024.13.1.51 Larrondo-Ureta, A., and Peña-Fernández, S. (2024). La formación de periodistas en la era de la inteligencia artificial: Aproximaciones desde la epistemología de la comunicación. Anu. ThinkEPI 18:e18e11. doi: 10.3145/thinkepi.2024.e18a11 Larsson, S., and Heintz, F. (2020). Transparency in artificial intelligence. Internet Po...
arXiv 2024
Reviewed August 15, 2026 · model on record in the stance chip above.
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