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The AI Incident Database editors updated their ingestion criteria and converted six former incidents—including GPT-3 medical chatbot experiments and Tesla Autopilot vulnerabilities—into 'issues' due to a lack of real-world harm.
On September 8, 2020, the Guardian published an op-ed generated by OpenAI’s GPT-3 text generating AI that included threats to destroy humankind. This incident has been downgraded to an issue as it does not meet current ingestion criteria.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
AI systems that fail to perform reliably or effectively under varying conditions, exposing them to errors and failures that can have significant consequences, especially in critical applications or areas that require moral reasoning.
The downgraded incidents primarily highlight technical limitations, vulnerabilities, and failures of AI systems (such as GPT-3's medical unviability and Tesla's lane recognition vulnerability) discovered during research and testing.
Other
Due to some other reason or is ambiguous
Other
Without clearly specifying the intentionality
Other
Without a clearly specified time of occurrence
The report is a compilation of multiple distinct events and administrative database updates, making a single causal entity, intent, or timing classification ambiguous.
The AI Incident Database updated its criteria, downgrading six historical entries to 'issues' because they occurred in research, mock, or controlled environments and caused no real-world harm. This administrative action and the underlying research experiments carry negligible national security implications.
The report is a compilation of six different AI systems (including GPT-3, Tesla Autopilot, and a facial recognition app) and does not characterize a single mode of operation for the described events.
High Risk: The report describes systems like Tesla's Autopilot (critical infrastructure/safety) and GPT-3 used for medical documentation and diagnosis support (healthcare), which fall under High Risk (Level 2) applications.
The report describes multiple AI systems, with text generation models like GPT-3 (used for medical documentation and writing op-eds) and autonomous driving systems (Tesla Autopilot) being the most prominent.
The report clearly outlines the administrative decision to downgrade several incidents to issues, explicitly stating the reasons (e.g., lack of real-world harm, academic findings, mock environments) for each. There is no ambiguity about the lack of actual harm caused by these events.
One could interpret the report as describing the individual incidents themselves rather than the administrative action of downgrading them, but the text explicitly frames them as converted issues.
Technology
Inconsistency in GPT-3
GPT-3 showed inconsistency and lack of scientific and medical expertise.
Adversarial Vulnerability
Tesla Autopilot lane recognition was vulnerable to three small stickers.
Poor Winograd Schema Performance
AI systems performed only 3% better than random chance in reasoning.
Data Inputs
Adversarial Input Samples
Crafted adversarial samples manipulated Tesla Autopilot lane recognition.
Lack of Medical Domain Training
GPT-3 lacked specific scientific and medical training data for healthcare.
Human Factors
Mock Patient Simulation
Researchers tested the medical chatbot using a fake patient scenario.
Human Manipulation of Output
Human editors manipulated GPT-3 outputs to exaggerate harm in reports.
Process and Methods
Experimental Deployment
Nabla deployed GPT-3 as an experiment for medical tasks without safeguards.
Lack of Output Filtering
The chatbot lacked mechanisms to filter out dangerous recommendations.
Management
Premature Testing in Healthcare
Testing GPT-3 for medical documentation and triage before validation.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
