Even experienced analysts make detection errors. This module covers the most common mistakes and how to avoid them.
False Positives: The Biggest Risk
A false positive occurs when human-written content is incorrectly flagged as AI-generated. This can have serious consequences in academic, legal, and journalistic contexts. Studies show that non-native English speakers and neurodivergent writers are disproportionately affected by false positives.
Critical Warning
Never rely on a single detection tool's output as definitive proof. Always cross-reference and consider context.
Common Error Patterns
The most frequent errors include over-reliance on confidence scores, ignoring the base rate problem, failing to account for editing and paraphrasing, and confirmation bias in analysis.
Building Better Judgment
Professional detection requires calibrated confidence. Learn to express uncertainty, document your methodology, and present findings with appropriate caveats. This is what separates amateur detection from forensic-grade analysis.