False positives
Also written: False positives vs. false negatives
When an AI system incorrectly flags a condition or event, leading to an unnecessary action or alert.
False positives happen when an AI system makes a mistake. It wrongly identifies something as a problem or a threat when there is no actual issue.
For example, a security AI might flag a normal computer process as a virus. This would be a false positive. The system then takes an unnecessary action, like blocking the process, which can disrupt normal work.
A false positive is an incorrect detection by a system, where a condition or event is flagged that does not actually exist. In machine learning, this often refers to a classification model incorrectly assigning an instance to a positive class when it belongs to the negative class. For instance, a spam filter might incorrectly classify a legitimate email as spam.
The tradeoff with reducing false positives is often an increase in false negatives, where real issues are missed. The acceptable rate of false positives depends heavily on the application's context and the cost of an incorrect alert versus a missed detection.
False positives are a recurring issue as AI systems are deployed in sensitive areas like cybersecurity and content moderation. Recent examples show AI classifiers and safeguards struggling to distinguish accurately, leading to unnecessary alerts or incorrect labeling. This highlights the ongoing challenge of balancing AI accuracy with real-world application, especially when dealing with nuanced data like text or potential security threats.
- Our approach to EU text provenance rulesOpenAI · OpenAI News ·
- The key to using AI in healthcareGoogle · Google Cloud Tech on YouTube ·
- Codename MDASH brings agentic AI cybersecurity to the US governmentMicrosoft · Microsoft Source ·
- Improving Fable 5 SafeguardsAnthropic · Anthropic News ·
- New AI classifier for indicating AI-written textOpenAI · OpenAI News ·
- Anthropic Claude
- Anthropic's Claude Fable 5 safeguards aim to reduce false positives, making the AI more helpful in health and education contexts.
- Microsoft Copilot
- Microsoft's cybersecurity AI aims to find exploitable software flaws, where false positives would mean incorrectly flagging non-existent vulnerabilities.
- OpenAI ChatGPT
- OpenAI has faced challenges with false positives in AI text detection, as seen with its retired classifier and the need for accurate watermarking.
- False negative
- When an AI system fails to detect a real problem or event.
- Type I error
- In statistics, this is the same as a false positive – rejecting a
- Type II error
- In statistics, this is the same as a false negative – failing to
The term 'false positive' originates from statistical hypothesis testing, specifically in the context of Type I errors. It has since been widely adopted across various fields, including signal detection theory and machine learning. Source ↗
Ask an AI chatbot like ChatGPT or Claude to write a short story about a talking cat. Then, ask it to summarize the story. If the AI incorrectly flags the story as containing harmful content or makes up a problem that isn't there, that would be a false positive.
The Playbook: reusable how-tos, prompts, configs and tips pulled out of the news.
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Questions people ask
- What is the opposite of a false positive?
- The opposite of a false positive is a false negative, where the AI fails to detect a real problem or event.
- Why are false positives a problem?
- False positives can lead to unnecessary actions, wasted resources, and reduced trust in AI systems. They can disrupt workflows or incorrectly flag benign content.
First seen on Subvolts Jan 31, 2023. Updated Oct 12, 2026. Written by an AI model from the items above and reviewed against them weekly; report an error.