Are AI Detectors Accurate?

Honest answer: accurate on obvious cases, shaky on everything else. Lab benchmarks advertise 98-99%+, real classrooms deliver meaningfully worse — because students aren't submitting raw ChatGPT output, and human writing styles overlap more with model output than vendors like to admit.

What the accuracy claims leave out

• Benchmarks test obvious AI. Raw model output against clean human prose is the easy case. Real submissions mix sources, quotes, edits and AI assistance.
• False positives are systematic, not random. Formal academic writing, non-native English and neurodivergent styles score high — the error lands on the same people repeatedly.
• Adversarial text breaks scores. Light editing moves numbers dramatically — which is why humanizing works (see the fundamentals).
• Scores disagree with each other. The same text routinely scores 15% on one detector and 60% on another.

What this means for you

If you're writing honestly and getting flagged: the detector's bias is the story, and your drafts plus an explainable second opinion are the fix (full playbook). If you're using AI and need to submit: know that scores are movable signals, not truths — measure before and after with a detector that shows which lines drove the score (free here), and fix those lines.

How to judge a detector yourself

1. Feed it text you wrote yourself — does it false-flag your style?
2. Feed it raw AI output — does it catch the easy case?
3. Feed it lightly-edited AI output — how does the score move?

That three-minute test tells you more than any vendor page. Related: what AI percentage is acceptable · detectors like Turnitin, compared · what actually moves scores.