Australian courts and regulators are scrutinising AI-driven decisions in consumer-facing systems, exposing companies to liability for unconscionable conduct, misleading practices, and lending violations even when no human intended harm. A series of recent rulings makes clear that the design, monitoring, and override mechanisms of automated systems are now central to consumer law risk.
Unconscionable conduct and systems liability
The High Court's 2024 decision in Productivity Partners Pty Ltd v ACCC; Wills v ACCC confirmed that unconscionable conduct can stem from system design choices, not just frontline misconduct. The case involved an enrolment process where safeguards were removed, making it foreseeable that vulnerable consumers would incur significant debts for no benefit. Liability did not hinge on an intent to harm - it was enough that harmful outcomes were reasonably foreseeable and controls were stripped away.
For AI systems, this translates into risks when pricing, eligibility, or collections models embed rigid rules or information asymmetries that forecast harsh results for vulnerable groups. The absence of effective escalation, override, or human review functions can expose a business to findings of systemic unconscionability.
Misleading or deceptive conduct beyond marketing claims
Misleading conduct under the Australian Consumer Law often turns on the overall impression a consumer forms, not just the fine print. In ACCC v Trivago NV, the Federal Court ordered Trivago to pay $44.7 million in penalties after finding its algorithm prioritised hotels based on which booking site paid the highest cost-per-click fee, rather than identifying the cheapest rates as the site represented. The court focused on the design, prominence, and adequacy of information presented to consumers.
When AI systems present automated pricing, ranking, or eligibility decisions, businesses must ensure they do not create a misleading overall impression. If the model operates on criteria that conflict with what users are led to believe, liability can arise through omission, framing, or timing - even if no single statement is false. For legal teams tasked with auditing these systems, resources like AI for Legal provide guidance on compliance automation and document review.
Lending, pricing and eligibility
AI-driven decision-making is widespread in credit assessment and dynamic pricing, where errors can rapidly scale. ASIC's recent action against Money3 Loans Pty Ltd illustrates the ongoing scrutiny. The Federal Court found that Money3 failed to make reasonable inquiries about borrowers' living expenses, relying on internal benchmarks instead of customer-specific bank transaction data. The court stressed that automated matrices cannot substitute for the inquiry and verification required by the National Consumer Credit Protection Act.
The case underscores that automation does not reduce the obligation to exercise human judgment - it raises the bar for proving that judgment was applied. Organisations must be able to explain how an AI model reaches individual decisions, what data it uses, when exceptions trigger human review, and how the process meets legal standards. Without such explainability, exposure under consumer law and responsible lending rules grows.
Why this matters for legal professionals
The core legal question is no longer whether AI systems are lawful in the abstract, but whether they are governed in practice. As the Federal Court observed in ASIC v Bekier [2026] FCA 196, "reliance on artificial intelligence to distil or summarise information cannot become a substitute for informed human judgment, nor an excuse for poorly disciplined systems of decision-making." Although that case concerned directors' duties, the same logic applies forcefully to consumer protection. Where automated systems determine price, eligibility, or access to essential services, companies must be able to show those systems are designed, monitored, and overridden in a way that meaningfully protects consumers.
For in-house counsel and compliance officers, this means governance is no longer optional - it is the linchpin of consumer law defence. If a company cannot explain how its AI decisions are made and cannot demonstrate safeguards against foreseeable harm, it will struggle in court. Legal and compliance professionals can build the expertise to manage these risks through structured learning, such as the AI Learning Path for Regulatory Affairs Specialists, which covers AI regulatory compliance and risk monitoring.
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