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AI incident #1109 ·

Year-long AI Surveillance Pilot in Two South Australian Aged Care Facilities Reportedly Overwhelmed Staff with False Positives

Open on the AI Incident Database 5 news reports Snapshot 7 Sep 2026

What happened

Between March 2021 and March 2022, an AI-enabled video and audio monitoring system was trialed in two South Australian aged care facilities. According to an independent audit commissioned by South Australia Health, the system produced over 12,000 false alerts, overwhelming staff and contributing to at least one missed real incident. The report concluded the system did not reach accuracy levels acceptable to staff or management during the pilot.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

News reports (5)

Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.

  1. Aged care CCTV trial using artificial intelligence resulted in too many false reports, minister says
    abc.net.au · Evelyn Manfield · AIID #5346
  2. ‘Botched’ aged care AI camera trial generates 12,000 false alerts
    innovationaus.com · Justin Hendry · AIID #5348
  3. South Australia's aged care AI trial produced 12,000 false alarms
    itnews.com.au · Ry Crozier · AIID #5349
  4. AI video surveillance care home trial produced too many false alarms, say auditors
    ifsecglobal.com · Ron Alalouff · AIID #5347
  5. Protecting the vulnerable, or automating harm? AI’s double-edged role in spotting abuse
    theconversation.com · Aislinn Conrad · AIID #5352

Who was involved

Alleged deployer
South Australia Health
Alleged developer
Sturdie Trade Services
Alleged harmed party
Residents And Staff Of Northgate House, Residents And Staff Of Mount Pleasant District Hospital, Northgate House, Mount Pleasant District Hospital

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Unintentional
Timing
Post-deployment
Harm level
—
Sectors
—
Countries
—

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

  • Reliability issues

    "Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...

    International AI Safety Report 2025 (Bengio2025)

  • Type 2: Bigger than expected

    Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Type 3: Worse than expected

    AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.

    TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)

  • Ethics and Morality Issues

    LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.

    Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)

  • Safe learning

    "AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."

    AGI Safety Literature Review (Everitt2018 )

  • Malign belief distributions

    "Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...

    AGI Safety Literature Review (Everitt2018 )

  • Meta-cognition

    "Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...

    AGI Safety Literature Review (Everitt2018 )

  • Misaligned consequentialist reasoning

    "As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the...

    The Ethics of Advanced AI Assistants (Gabriel2024)

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