Tesla Allegedly Misled Customers about Autopilot and FSD Capabilities

Laws on record for this risk area

Policy angle: Classified under Misinformation (False or misleading information) in the MIT AI Risk Repository taxonomy; 5 instruments on record cover this risk area.

• India DPDP Act India · Partially applicable · binding
• Law on Artificial Intelligence (2025) Kazakhstan · Adopted · binding
• Law No. 132/2025 on artificial intelligence Italy · In force · binding
• EU AI Act European Union · Partially applicable · binding
• Texas Responsible AI Governance Act (TRAIGA) Texas (United States) · In force · binding

Matched from the record's risk category and country to the instruments in effect recorded here: a law listed covers the area, which is not a finding that it applies to this incident. A reviewer can correct the match in the repository (data/external/incident_overrides.yaml).

News reports (6)

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

1. 5 Aug 2022
   California DMV accuses Tesla of falsely advertising Autopilot and Full Self-Driving features

   latimes.com · Russ Mitchell
2. 6 Aug 2022
   California DMV accuses Tesla of making false claims about Autopilot and Full-Self Driving

   theverge.com · Emma Roth
3. 8 Aug 2022
   California DMV Accuses Tesla Of False Advertising For Autopilot, FSD

   insideevs.com · Steven Loveday
4. 27 Oct 2022
   Exclusive: Tesla faces U.S. criminal probe over self-driving claims

   reuters.com · Mike Spector, Dan Levine
5. 18 Jan 2023
   Remember that 2016 video of a Tesla driving itself? It was staged.

   mashable.com · Stan Schroeder
6. 28 Jan 2023
   Elon Musk is being investigated by the SEC for Tesla self-driving claims, report says

   techcrunch.com · Darrell Etherington

Who was involved

Alleged deployer

Tesla

Alleged developer

Tesla

Alleged harmed party

California Department Of Motor Vehicles, Tesla Customers, California Residents

Classification (MIT AI Risk Repository taxonomy)

Risk domain

Misinformation

Risk subdomain

3.1 False or misleading information

Causal entity

Human

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 3.1.

• Pursuing Consistent Context

  "LLMs have been demonstrated to pursue consistent context [129]–[132], which may lead to erroneous generation when the prefixes contain false information. Typical examples include sycophancy [129], [130], false demonstra...

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

• Defective Decoding Process

  In general, LLMs employ the Transformer architecture [32] and generate content in an autoregressive manner, where the prediction of the next token is conditioned on the previously generated token sequence. Such a scheme...

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

• Noisy Training Data

  "Another important source of hallucinations is the noise in training data, which introduces errors in the knowledge stored in model parameters [111]–[113]. Generally, the training data inherently harbors misinformation....

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

• Untruthful Content

  "The LLM-generated content could contain inaccurate information"

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

• Hallucinations

  "LLMs generate nonsensical, untruthful, and factual incorrect content"

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

• Faithfulness Errors

  "The LLM-generated content could contain inaccurate information" which is is not true to the source material or input used

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

• Factuality Errors

  "The LLM-generated content could contain inaccurate information" which is factually incorrect

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

• Knowledge Gaps

  "Since the training corpora of LLMs can not contain all possible world knowledge [114]–[119], and it is challenging for LLMs to grasp the long-tail knowledge within their training data [120], [121], LLMs inherently posse...

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

Incidents in the same risk subdomain

• 12 May 2026Nonfiction Book 'The Future of Truth' Reportedly Included AI-Generated and Misattributed Quotations
• 6 May 2026Claude Console Reportedly Generated Phantom Legal Quotations in Trump Layoffs Court Filing
• 21 Apr 2026Purportedly AI-Enhanced Images of Iranian Women Protesters Were Reportedly Spread With Unverified Execution Claims
• 10 Apr 2026South Africa Draft National AI Policy Reportedly Included Fictitious References Believed to Be AI Hallucinations
• 8 Apr 2026Purportedly AI-Generated Image Reportedly Misled Daejeon Authorities Searching for Escaped Wolf Neukgu
• 17 Mar 2026Gemini and Grok Reportedly Misidentified Authentic Minab School-Strike Graveyard Photo as Unrelated Disaster Imagery

Other incidents involving Tesla

• 17 Feb 2025Tesla Cybertruck Operating in Full Self-Driving Mode Reportedly Crashes into Pole in Nevada After Failing to Merge
• 7 Jan 2025Tesla's 'Actually Smart Summon' Feature Reportedly Linked to Multiple Parking Lot Collisions
• 23 Jun 2023Tesla on Autopilot Struck Parked Work Truck on Highway in Pennsylvania
• 15 May 2023Tesla Failed to Yield to Detected Pedestrian on Crosswalk, Reportedly Violated Traffic Law
• 17 Mar 2023Tesla Allegedly on Autopilot Struck High School Student Exiting School Bus
• 28 Dec 2022Tesla Driver Put Car on Autopilot Before Falling Asleep in Germany