AI Detection
AI DetectionThe process of identifying whether a piece of text was written by an AI model rather than a human.
AI detection uses statistical analysis and machine learning classifiers to determine whether text originated from a language model. Most detectors were trained on outputs from GPT-3, GPT-4, and similar models, teaching them to recognize patterns those models consistently produce: low perplexity, narrow token distributions, and flat burstiness across sentences.
The core limitation of AI detection is that it is probabilistic, not definitive. A detector reporting '94% AI' is expressing a confidence level, not a fact. False positives are common — technical writing, ESL prose, and highly formal academic text all score as suspicious. This is why Turnitin and GPTZero have both faced criticism from educators over wrongful academic misconduct flags.
For users producing AI-assisted content, understanding how detection works is the first step to working around it intelligently. Coda One's AI Detector shows you exactly how your text scores before you publish or submit, giving you the data to decide whether humanization is necessary.
Real-World Example
A freelance writer ran their AI-assisted blog post through Coda One's AI Detector and got a 78% AI probability score — high enough to revise before submitting to a client with strict content policies.
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FAQ
What is AI Detection?
The process of identifying whether a piece of text was written by an AI model rather than a human.
How is AI Detection used in practice?
A freelance writer ran their AI-assisted blog post through Coda One's AI Detector and got a 78% AI probability score — high enough to revise before submitting to a client with strict content policies.
What concepts are related to AI Detection?
Key related concepts include AI Detection Score, Perplexity Score, Burstiness, GPTZero, Turnitin, Originality.ai. Understanding these together gives a more complete picture of how AI Detection fits into the AI landscape.