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Texas Homeowner Reportedly Spent $3,000 to Contest AI-Flagged Warning of Insurance Nonrenewal

1 news report Synced from source · record last edited 3 Sep 2026

In brief

An AI system built by Nearmap and CAPE Analytics and deployed by Travelers Insurance, State Farm and 2 others allegedly harmed Tracy Gartenmann, Homeowners in Texas and 1 other.

Risk domain
AI system safety, failures, and limitations Lack of capability or robustness
Occurred
Coverage
1 reportMay 2025

What happened

In Texas, homeowners including Tracy Gartenmann reported warnings against insurance nonrenewals based on aerial imagery flagged by AI systems. Gartenmann's insurer, Travelers, reportedly cited overhanging trees. She reportedly spent $3,000 to address the issue and retain coverage. While the AI system appears to have functioned as intended, critics argue it lacks adequate human oversight and imposes material and emotional burdens on policyholders. Other cases reportedly involved disputed roof ass

Editor's notes

The KUT investigation was published on 05/13/2025 and references multiple incidents from 2023 onward. The specific case of Tracy Gartenmann occurred in January 2025, while other cases involving State Farm and Nationwide span from 2023 to early 2025, based on homeowner complaints and regulatory filings, according to the reporting. See also Incident 1082 for a variant of this incident that occurred in March 2024 in California.

Laws that address this harm

Policy angle: Classified under AI system safety, failures, and limitations (Lack of capability or robustness) in the MIT AI Risk Repository taxonomy; 5 recorded instruments address this use case.

Matched from the record's risk domain and country to the instruments recorded here. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (1)

Titles link to the original publisher; report text is not reproduced here.

  1. In Texas, insurers are watching your home from above. It could cost you coverage.
    kut.org · Audrey McGlinchy

Who was involved

Alleged developer
Nearmap CAPE Analytics
Alleged harmed party
Tracy Gartenmann Homeowners in Texas Homeowners affected by AI-assisted insurance nonrenewals

AI systems implicated

Nearmap aerial analysis toolsEnterprise AI systemsCAPE roof risk scoring systemAlgorithmic roof condition classificationAI-enabled decision support systemsAI-assisted aerial imagery risk assessment

Classification (MIT AI Risk Repository taxonomy)

Causal entity
AI
Intent
Intentional
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 )

  • Technical and operational risks

    "To date, technical limitations and vulnerabilities are present in most generative AI models in various contexts. Consequently, malicious users find it easier to breach an AI system’s safety and ethical guardrails to e...

    Regulating under Uncertainty: Governance Options for Generative AI (G'sell2024)

Linked by editors or by text similarity in the source dataset.

Incidents in the same risk subdomain

All incidents in this subdomain

Source record: incident #1083 on the AI Incident Database · all 1 report