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AI Content Detection False-Positive Analyzer.

Examine diagnostic accuracy using Bayes Theorem to calculate the probability a student or employee was wrongfully accused of using AI due to detection tool structural bias.

## The Danger of AI Detectors

Educational institutions and content publishers frequently deploy AI detectors to punish synthetically generated text. Unfortunately, administrators suffer from the **Base Rate Fallacy**—failing to understand that even a 95% accurate tool can result in massive wrongful accusations if the inherent baseline usage of AI in the general population is low.

### FAQ

**Q: What is the Base Rate Fallacy?**
A: If a disease infects 1 in 10,000 people, and a test is 99% accurate, a positive test result doesn't mean you have a 99% chance of having the disease. Because the disease is so rare, the 1% false positive margin is triggered far more frequently across thousands of healthy people. AI detectors suffer this exact same mathematical fate against innocent human writers.

**Q: Why do ESL students trigger false positives?**
A: AI detectors measure "Perplexity" (how predictable word choices are). Non-native English speakers typically write using structurally simple, highly predictable vocabulary, which perfectly mimics the statistical output of ChatGPT, placing them at severe risk of wrongful academic penalization.