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Incident 159: Tesla Autopilot’s Lane Recognition Allegedly Vulnerable to Adversarial Attacks

Description: Tencent Keen Security Lab conducted security research into Tesla’s Autopilot system and identified crafted adversarial samples and remote controlling via wireless gamepad as vulnerabilities to its system, although the company called into question their real-world practicality. This incident has been downgraded to an issue as it does not meet current ingestion criteria.

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Alleged: Tesla developed and deployed an AI system, which harmed Tesla drivers.

Incident Stats

Incident ID
159
Report Count
1
Incident Date
2019-03-29
Editors
Sean McGregor, Khoa Lam
Applied Taxonomies
MIT

MIT Taxonomy Classifications

Machine-Classified
Taxonomy Details

Risk Subdomain

A further 23 subdomains create an accessible and understandable classification of hazards and harms associated with AI
 

7.3. Lack of capability or robustness

Risk Domain

The Domain Taxonomy of AI Risks classifies risks into seven AI risk domains: (1) Discrimination & toxicity, (2) Privacy & security, (3) Misinformation, (4) Malicious actors & misuse, (5) Human-computer interaction, (6) Socioeconomic & environmental harms, and (7) AI system safety, failures & limitations.
 
  1. AI system safety, failures, and limitations

Entity

Which, if any, entity is presented as the main cause of the risk
 

AI

Timing

The stage in the AI lifecycle at which the risk is presented as occurring
 

Pre-deployment

Intent

Whether the risk is presented as occurring as an expected or unexpected outcome from pursuing a goal
 

Unintentional

Incident Reports

Reports Timeline

Incident OccurrenceAI Incident Database Incidents Converted to Issues
AI Incident Database Incidents Converted to Issues

AI Incident Database Incidents Converted to Issues

github.com

AI Incident Database Incidents Converted to Issues
github.com · 2022

The following former incidents have been converted to "issues" following an update to the incident definition and ingestion criteria.

21: Tougher Turing Test Exposes Chatbots’ Stupidity

Description: The 2016 Winograd Schema Challenge highli…

Variants

A "variant" is an incident that shares the same causative factors, produces similar harms, and involves the same intelligent systems as a known AI incident. Rather than index variants as entirely separate incidents, we list variations of incidents under the first similar incident submitted to the database. Unlike other submission types to the incident database, variants are not required to have reporting in evidence external to the Incident Database. Learn more from the research paper.

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