AI incident #1083 ·
Texas Homeowner Reportedly Spent $3,000 to Contest AI-Flagged Warning of Insurance Nonrenewal
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 (AI Incident Database)
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.
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 (1)
Coverage catalogued by the AI Incident Database. Titles link to the original publisher; the text is not reproduced here.
Who was involved
- Alleged deployer
- Travelers Insurance State Farm Nationwide American Mercury Insurance Group
- 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)
- Risk domain
- AI system safety, failures, and limitations
- Risk subdomain
- 7.3 Lack of capability or robustness
- 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...
- 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.
- 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.
- 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.
- 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)."
- 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...
- 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...
- 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...
Related incidents on the AI Incident Database
Linked by AIID editors or by its text-similarity model.
Incidents in the same risk subdomain
- Purported AI Name-Reading System Reportedly Skipped and Misannounced Graduates at Arizona's Glendale Community College Commencement
- PocketOS Production Database Was Reportedly Deleted by Cursor AI Agent Running Claude Opus 4.6
- Baidu Apollo Go Robotaxis Stopped in Traffic During Reported System Failure in Wuhan, Stranding Some Passengers
- Purportedly AI-Enabled Targeting System Was Reportedly Implicated in Deadly U.S. Strike on Iranian Primary School
- Claude Code Agent Reportedly Deleted DataTalks.Club Production Infrastructure, Database, and Snapshots via Terraform
- Purportedly AI-Generated Sepsis Alert Reportedly Prompted Potentially Inappropriate IV Fluid Administration for a Dialysis Patient, Averted by Clinician Intervention