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Auto Insurers Allegedly Are Surreptitiously Collecting and Scoring Driver Data

2 news reports Synced from source · record last edited 3 Sep 2026

In brief

An AI system built by MyRadar, Life360 and 2 others and deployed by USAA, Toyota and 9 others allegedly harmed Privacy-conscious individuals, Privacy and 8 others.

Risk domain
Privacy & Security Compromise of privacy by obtaining, leaking or correctly inferring sensitive information
Occurred
Coverage
2 reportsJun 2024

What happened

The insurance industry allegedly uses AI and telematics to score drivers based on behaviors tracked by automakers and apps like Life360. Data, often collected without clear consent, may affect insurance rates and raises privacy concerns. Consumers are largely unaware of this surveillance, leading to potential misuse and discrimination based on driving habits or socioeconomic factors.

Laws that address this harm

Policy angle: Classified under Privacy & Security (Compromise of privacy by obtaining, leaking or correctly inferring sensitive information) 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 (2)

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

  1. Is Your Driving Being Secretly Scored?
    nytimes.com · Kashmir Hill
  2. Car insurance companies secretly collecting driver data with the help of phone apps: report
    nypost.com · Ariel Zilber

Who was involved

Alleged harmed party
Privacy-conscious individuals Privacy People with poor credit scores MyRadar users Lower-income workers Life360 users Economically vulnerable people Drivers unaware of data collection Drivers Consumers affected by insurance rates

AI systems implicated

Smartphone-based driving behavior analytics systemsInsurance telematics systemsDriver risk-scoring algorithmsConnected vehicle telematics systemsConnected Analytic Services vehicle data platformArity IQ networkArity driving scoreAI-enabled decision support systems

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 2.1.

  • Risks to privacy

    "General- purpose AI models or systems can ‘leak’ information about individuals whose data was used in training. For future models trained on sensitive personal data like health or financial data, this may lead to partic...

    International Scientific Report on the Safety of Advanced AI (Bengio2024)

  • Risks to privacy

    "General- purpose AI systems can cause or contribute to violations of user privacy. Violations can occur inadvertently during the training or usage of AI systems, for example through unauthorised processing of personal d...

    International AI Safety Report 2025 (Bengio2025)

  • Private Training Data

    "As recent LLMs continue to incorporate licensed, created, and publicly available data sources in their corpora, the potential to mix private data in the training corpora is significantly increased. The misused private d...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Association in LLMs

    "Association in LLMs refers to the capability to associate various pieces of information related to a person. According to [68], [86], given a pair of PII entities (xi , xj ), which is associated by a model F. Using a pr...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Privacy Leakage

    "Privacy Leakage means the generated content includes sensitive personal information"

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Memorization in LLMs

    "Memorization in LLMs refers to the capability to recover the training data with contextual prefixes. According to [88]–[90], given a PII entity x, which is memorized by a model F. Using a prompt p could force the model...

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Privacy Leakage

    "The model is trained with personal data in the corpus and unintentionally exposing them during the conversation."

    Risk Taxonomy, Mitigation, and Assessment Benchmarks of Large Language Model Systems (Cui2024)

  • Privacy and regulation violations

    "Some of the broken systems discussed above are also very invasive of people’s privacy, controlling, for instance, the length of someone’s last romantic relationship [51]. More recently, ChatGPT was banned in Italy over...

    Navigating the Landscape of AI Ethics and Responsibility (Cunha2023)

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 #733 on the AI Incident Database · all 2 reports