Australia: AI Assisted Claims Are Here

In a recent address, the President of Australia’s Fair Work Commission (FWC) described a sharp, AI‑driven surge in applications, highlighting how large language models (LLMs) can produce ready‑to‑file claims within minutes, including at times with invented facts. Near‑zero drafting costs, polished legal tone and overconfident narratives are reshaping employer investigations, discipline processes, and dispute resolution across Australia. Although AI-assisted tools have made it easier for employees to make claims that, on the surface, may appear more legitimate, employers confronting this new reality are reminded to focus on the merits of the claims themselves, not how they may have been generated. Below are some pragmatic adjustments employers can make to focus on the basics: facts, authority, and process. 

Expect more plausible (but weak) internal grievances and external claims

AI dramatically reduces the friction in raising a grievance or filing a claim. The FWC has observed record workloads since the mainstream release of LLMs in late 2022. These matters often appear sophisticated but, on closer examination, can be light on merit: frequently using boilerplate language alleging (for example) procedural unfairness. The FWC intends to introduce disclosure requirements into forms to make parties vouch for accuracy and provide hyperlinks to any authorities cited. 

Whether responding to an internal employee complaint or an external claim, the employer’s initial screen and triage therefore must separate confidence from credibility. Human decision makers are prone to “confidence bias,” which is the tendency to give more weight to statements delivered with certainty, even where accuracy is low. LLMs can default to confidently worded outputs, so employers must rely on structured factual verification rather than tone alone in assessing the validity of the claims asserted.

“AI‑Aware” investigations: Techniques to test grievances and claims without escalation

• Request factual particulars, not admissions about tool use

Ask for specific dates, locations, individuals involved and documents.

• Build a “factual integrity matrix”

For each allegation, record the claim, its source, any corroboration and any discrepancies using emails, logs, rosters, chat threads or access records. This must be carried out within lawful monitoring rules and, where required, informed by privacy impact assessments.

• Meet with the employee to hear the account in their own words

Hold an early meeting and ask the complainant to recount events without scripts. Unstructured recall tends to surface inconsistencies and clarify what the complaint is actually about.

• Require sources for legal citations

Ask for hyperlinks to any authorities the employee relies on. Fabricated or irrelevant references surface quickly, reducing wasted effort.

• Be mindful of confidence bias

Gather the facts before applying analysis and use checklists to limit the “pull” of confident prose.

Update policies: Lawfully monitor

Appropriate monitoring may provide advance notice of grievances before they morph into an external claim. In Australia, workplace surveillance is generally permitted subject to specific state or territory laws. For example, in New South Wales, employers must provide written notice, maintain a clear policy and comply with strict limits on covert monitoring. 

Rethink discipline: From “the data say” to “evidence + human review”

Where monitoring or analytics feed into disciplinary decisions, unions and regulators will scrutinize for accuracy, proportionality and human oversight. In New South Wales, recent legislative reform will allow unions to scrutinize workplace algorithms where a breach of work health and safety laws is suspected.

Dispute resolution: Faster triage, earlier settlements and clearer records

The FWC is compressing conciliations and warning that cases light on merit will be closed quickly. Rather than debating AI‑drafted rhetoric, employers should respond with evidence‑first triage and short, well‑documented position statements anchored to verifiable records.

For employers globally, this means:

• preserving evidence early (email headers, audit logs, rosters) within lawful frameworks.
• using targeted requests for particulars to narrow issues rapidly. Consider filing early objections to the Fair Work Commission where claims are facially deficient.
• adopting disclosure expectations (e.g., hyperlinks to authorities and factual accuracy confirmations) in internal processes to deter speculative claims.

Final thought

AI will continue to increase both the volume and the “polish” of internal grievances and external claims. The employer response should not be to debate AI’s role, but to raise the bar on accuracy, transparency and procedural rigor. 

Lawful monitoring, robust privacy practices, structured investigations and evidence‑led dispute resolution are now essential to ensure meritorious matters are resolved and opportunistic claims are filtered out.