AI incident #1082 ·
California Homeowner Reportedly Loses Insurance After Purported Aerial Imagery-Based Roof Assessment
What happened
A California homeowner's insurance policy was reportedly not renewed after CSAA relied on aerial imagery, purportedly analyzed by an AI system, to assess the roof as having reached the end of its life. The homeowner, Cindy Picos, reportedly commissioned an independent inspection suggesting the roof had a decade of remaining life, but CSAA declined to reverse the decision. The insurer reportedly did not provide the imagery for review.
Editor's notes (AI Incident Database)
The article was published on 04/06/2024 and states that Cindy Picos was dropped by her insurer "last month," indicating the primary harm likely occurred in March 2024. Additional cases cited occurred between 2022 and early 2024, but the Picos case is the focal event. Refer as well to Incident 1083, which is a variant of this focusing on Texas homeowners. This incident ID was created 05/31/2025.
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
- CSAA Insurance Group
- Alleged developer
- Vexcel Group Unspecified developer of aerial imagery risk analysis system
- Alleged harmed party
- Homeowners affected by AI-assisted insurance nonrenewals Homeowners Cindy Picos
AI systems implicated
Enterprise AI systemsAlgorithmic 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...
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Linked by AIID editors or by its text-similarity model.
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