Leadership1 distinct publisher3 min readUpdated
The technology trial used to justify Australia's under-16 social media ban carries citations critics call AI-fabricated. Its authors conceded ChatGPT use only after being shown link metadata.
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The authors of the technology trial underpinning Australia's under-16 social media ban have conceded ChatGPT was used in editing the report, while denying that citation errors in it were AI hallucinations [1]. This is not a brochure that can be quietly corrected: the communications minister, Anika Wells, presented the report as showing "many effective options" for checking ages, and it paved the way for the ban to take effect in December last year [4][5].
The $3.48m age assurance technology trial was run by the UK-based Age Check Certification Scheme (ACCS) [2], testing technologies platforms could use to comply with the ban [3]. Most of the report rests on the authors' own testing of available products [6]. The problems sit in one chapter on "emerging technologies" that cites journal papers [7]. A submission to an ongoing Senate inquiry into strengthening the law flagged at least two citations that "appear to be AI hallucinated, rather than being based on real sources" [8]. Guardian Australia's own analysis of that section found six references containing errors [10]: DOIs linking to papers that do not exist, DOIs pointing to the wrong papers, author, journal and year combinations matching no known reference, and DOI links to papers that did not say what the report said they said [11].
The sequence after that is the part worth reading twice. ACCS initially denied using AI in the report at all [9], stating that each citation had been "checked as genuine links and reports" and confirmed as relevant [c9b]. It then supplied clarifications and replacement references, and those contained errors too, with years, authors and journal titles that did not match the original citations [12]. One cited paper was recorded as accessed in March 2025, but a lead author told the Guardian it was not publicly accessible until it was published in June [13], roughly three months later [1]; the same author said the person ACCS named as lead author, "Jamil", had never been on the paper, and that the report had summarised it incorrectly [13]. Asked about "Weber et al. 2011", which could not be found in the journal cited, ACCS pointed to a Monash University study, which contained no reference attributed to Weber [14].
The concession came only after the Guardian identified four links in parts E and K of the report carrying metadata showing ChatGPT as their source [15]. ACCS's position is that including that metadata amounted to disclosure and, since the links were correct, there was no problem [16], and that checking "was all done by human verification" [17]. Its account of the use is narrow: rewriting some paragraphs more succinctly, not originating research [18].
There is a price list for this already. Deloitte refunded part of a $440,000 government contract last year after errors were identified and the firm admitted using AI [19]. The ACCS trial cost roughly eight times that contract [2].
The department said it is examining the concerns raised and will engage with ACCS as appropriate [20], while questions to Wells' office were redirected there [21]. At a Senate inquiry hearing on Friday, officials said ACCS had told them the errors were caused by links breaking that previously worked [22]. Two things to watch: whether the department re-verifies the emerging technologies chapter itself rather than accepting that explanation, and whether any of the $3.48m follows the Deloitte precedent back to the Commonwealth [19][2].
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Ranked by verification strength, evidence, and original report placement.
Deloitte last year refunded part of its $440,000 contract with the Australian government after errors were identified and the firm admitted to using AI.
The authors of a report testing the technology underpinning Australia's social media ban conceded ChatGPT was used in editing, but denied that a number of citation errors in the report were due to AI hallucinations.
The age assurance technology trial cost $3.48m and was run by the UK-based Age Check Certification Scheme (ACCS) last year.
The trial tested various types of technology that could be used by social media platforms as part of Australia's under-16 social media ban.
Communications minister Anika Wells heralded the report as showing "many effective options" for checking people's ages.
The report paved the way for the under-16 social media ban to come into effect in December last year.
Evidence-backed comparisons of source perspectives and observed adoption signals. Read the methodology
Which Builder, Operator, and Investor concerns the observed source mix emphasized—not a truth score.
Evidence, demonstrated adoption, hype gap, incentives, and confidence are assessed independently, each on its own current evidence. How these are measured.
Documented but single-publisher
The evidentiary core is unusually concrete for a single-source cluster: an enumerated set of six defective references with described failure types, four links carrying ChatGPT metadata, on-record confirmation from a cited paper's lead author that the access date preceded publication and that the named lead author was never on the paper, a Monash paper containing no Weber reference, and named department testimony at a Senate hearing. What limits the score is that no independent publisher corroborates the analysis, the causal attribution to AI hallucination remains disputed, and no official finding has been published.
Disputed evidence base already in force
Adoption here is the uptake of the contested evidence base into live policy rather than uptake of a product: the report was delivered from a $3.48m commissioned trial, was publicly used by the minister to assert many effective age-check options, and preceded the under-16 ban taking effect in December last year, which is now the subject of a Senate inquiry seeking to strengthen it. Scored high but not maximal because the supplied source gives no measure of platform-level implementation of the tested technologies.
Mildly overstated causal framing
The documented facts - six erroneous references, ChatGPT metadata in four links, a denial that shifted, and corrections that repeated the defects - are solid. The framing that runs slightly ahead of them is the causal label 'AI hallucinations': ACCS concedes only editorial rewriting, no independent finding attributes the citations to a model, and the errors sit in one 'emerging technologies' chapter of a roughly 1,000-page report whose bulk is hands-on testing. Pointing the other way, the government's own 'many effective options' characterisation and the department's 'handful of errors' framing understate an unverified explanation, which is why the gap is small rather than large.
Contract, political and precedent pressure
Incentives are visible on the record. ACCS holds a $3.48m government engagement and faces an explicit refund demand modelled on Deloitte's partial refund of a $440,000 contract after admitting AI use - a direct financial reason to deny AI involvement and to attribute defects to broken links. The minister publicly staked the ban's justification on the report, and her office redirected questions to the department, which minimised the issue as a handful of errors while not verifying the contractor's account. The publisher's incentive is a high-salience accountability scoop.
Well-documented, single-publisher
Confidence is bounded by the single-publisher cluster and by the absence of any concluded departmental or parliamentary finding, but lifted by the specificity of the reporting: named officials quoted from a Senate hearing, direct spokesperson quotations, artefact-level link metadata, and independent confirmation from a cited paper's lead author. The existence and nature of the citation defects can be relied on more firmly than their cause or their consequences.
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