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Federal court orders $45,000 in costs after linking filing anomalies to AI use

A Federal Court judge has ordered a self-represented litigant to pay $45,000 in costs to Stantec Australia, citing the likely indiscriminate use of AI in court filings that contained non-existent submissions.

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Source-page capture accompanying Federal court orders $45,000 in costs after linking filing anomalies to AI use
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onnotice.com.au
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onnotice.com.auhttps://onnotice.com.au/news/stantec-wins-45-000-costs-over-likely-ai-use-in-filings/
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Key terms

Artificial Intelligence (AI)
The broad field of building systems that perform tasks requiring pattern recognition, reasoning, language, or decision-making.
Benchmark
A standardized test or dataset used to measure and compare model performance.
Citations
References to source passages or documents included in a model's response to support its claims.
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What happened

Justice Wheelahan of the Federal Court of Australia ordered a former employee to pay $45,000 in legal costs to Stantec Australia following a failed judicial review application. The court determined the applicant’s filings were incoherent and contained 'apparent hallucinations,' specifically referencing written submissions that did not exist. While the applicant claimed he used AI only for grammar and spelling, the judge concluded that the document anomalies were likely the result of the 'indiscriminate use of artificial intelligence.'

The judgment, [2026] FCA 1415, delivered on 24 September 2026, followed the dismissal of a worker's application for judicial review of Fair Work Commission decisions. Stantec Australia sought costs, arguing the case was vexatious and lacked merit.

Justice Wheelahan noted that the applicant's filings were 'incoherent in many respects' and contained references to written submissions that did not exist. The applicant, a former human resources professional, admitted to using AI for grammar and spelling checks.

The court rejected the applicant's plea of financial hardship, noting a lack of reliable evidence, and emphasized that self-representation is not a privilege that immunizes a party from the consequences of their actions.

Stantec had initially sought significantly higher costs, but the court fixed the amount at $45,000 after applying a 'broad-brush' assessment and reducing the claim due to errors in the company's own quantification evidence.

Source details: onnotice.com.au ↗

Why it matters

This ruling highlights the growing judicial scrutiny of AI-generated content in legal proceedings. By linking 'hallucinations'—a common failure mode of large language models—to a specific costs order, the court has signaled that self-representation does not shield litigants from the consequences of using AI tools that produce inaccurate or fabricated legal references. The decision reinforces the principle that litigants are responsible for the veracity of their submissions, regardless of the technology used to draft them, and establishes a precedent for how courts may penalize the submission of AI-generated 'slop' that wastes judicial and opposing party resources.

The case serves as a warning that the use of AI in legal drafting is subject to the same standards of accuracy as human-authored work. The court's explicit mention of 'hallucinations' suggests that judges are increasingly identifying the specific artifacts of AI usage.

By invoking s 570 of the Fair Work Act, the court demonstrated that the 'unreasonable act' of submitting AI-generated content that lacks forensic purpose or contains fabricated references can trigger cost orders, even in jurisdictions where costs are typically restricted.

The ruling underscores that professional background—in this case, the applicant's HR experience—may influence a judge's assessment of whether a litigant should have known better than to rely on unverified AI outputs.

Interactive Mechanism

Interactive Mechanism: How It Actually Works

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System Requirements:
Best ArchitecturePure RAGRecommended pattern
Hallucination RiskVery LowGrounding efficacy
Update Cost$0 (Vector sync)Ongoing maintenance
Core takeaway: Fine-tuning teaches models how to speak (form, style, syntax); RAG teaches models what to say (verifiable facts). Never use fine-tuning alone for factual memory.
Interactive Concept Check+10 Points
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Impossibility results in algorithmic fairness (e.g. Kleinberg et al., Chouldechova) show what?

What to watch next

Watch for further Australian court rulings that explicitly address AI-generated errors in filings. As courts become more adept at identifying AI-induced hallucinations, such as phantom or non-existent submissions, there may be a shift toward stricter certification requirements for legal documents. Additionally, monitor whether this $45,000 cost order influences future litigation strategies for companies facing self-represented parties who rely on AI, potentially setting a for recovering costs incurred due to AI-related procedural inefficiencies.

Future developments in Australian industrial relations law regarding the admissibility and verification of AI-assisted filings.

Whether other courts adopt similar 'broad-brush' cost assessments when dealing with the increased administrative burden caused by AI-generated legal errors.

Potential policy shifts or practice notes from the Federal Court of Australia regarding the disclosure of AI usage in court documents.

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