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You can call a text classifier from a few lines of Python, but its result is only a model’s estimate—not proof that text was written by AI or by a particular system. OpenAI’s own classifier was withdrawn for low accuracy, and research on code detectors does not establish a reliable shortcut for arbitrary text or source code.
A three-line example is a model call, not an authorship test
There is no general-purpose three-line Python test that can reliably establish whether a passage was generated by AI. A short snippet can load or query a particular classifier, but its output depends on what that detector was trained and evaluated to recognize, the language and length of the input, and whether the text has been edited.
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The available Hugging Face model card describes an older OpenAI RoBERTa model as a GPT-2 text detector and explicitly warns against using it to detect ChatGPT misconduct. That makes it unsuitable to present as a current, general-purpose answer to “who wrote this?” rather than as an experiment with a specific historical model: Hugging Face model card.
Why OpenAI’s former classifier should not be your Python solution
OpenAI discontinued its AI Text Classifier on July 20, 2023, citing low accuracy. On one English challenge set, it correctly labeled 26% of AI-written text as “likely AI-written” and incorrectly labeled 9% of human-written text that way. Those figures describe that evaluation—not the performance of every detector, nor present-day performance across languages and writing tasks. OpenAI also said its classifier was very unreliable below 1,000 characters, performed significantly worse outside English, and was unreliable on code. It warned that editing could evade detection and that the tool could be confidently wrong on inputs unlike its training data. OpenAI’s classifier announcement and limitations.
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OpenAI summarized the broader problem this way: “While it is impossible to reliably detect all AI-written text, we believe good classifiers can inform mitigations for false claims that AI-generated text was written by a human.” The statement accompanied a classifier that OpenAI later withdrew; it is not a guarantee that classifiers can establish authorship.
What code-focused detector research does—and does not—show
Text detectors and source-code detectors address different tasks. A detector trained on prose should not be assumed to work on Python source, and a result for one programming-language dataset does not automatically transfer to another generator, codebase, or evaluation set.
- A 2024 ICSE study abstract reports that existing detectors performed poorly on its human-versus-AI Python solutions. ICSE study abstract.
- The GPTSniffer paper reports better results than two baselines in its own evaluation. That is evidence about that paper’s task and evaluation, not validation of a three-line, general-purpose detector for arbitrary modern source code. GPTSniffer paper abstract.
These findings cannot be turned into a sound “best detector” ranking without matching the task, datasets, languages, input conditions, and evaluation methods. The available evidence does not establish a present-day winner for general Python use.
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If you are evaluating a particular model or service, check these details before treating a score as informative:
- Target: Does it classify prose or source code? Which generator era or family was represented in its training and tests?
- Evaluation evidence: What language, population, and dataset were tested? Were human and machine-written examples comparable?
- Input conditions: How long must a sample be, and how does editing or transformation affect results? OpenAI’s below-1,000-character warning applied to its retired classifier, not to all detectors.
- Error costs: A false positive can wrongly cast suspicion on a human writer; a false negative can miss generated text. An output label or score does not resolve either risk on its own.
- Decision stakes: Exploratory triage is different from an educational or disciplinary decision. OpenAI said its retired classifier should not be a primary decision-making tool, and the Hugging Face model card warns against using its model for grave misconduct allegations.
A package name is not evidence that a detector remains supported. The openai-detector package listing describes a wrapper for OpenAI’s former classifier; since that classifier was withdrawn, the wrapper should not be presented as a currently available, supported detection service. Package listing.
Can I ask ChatGPT whether it wrote a passage?
No. OpenAI says ChatGPT has no knowledge of whether it generated text someone pastes into a conversation and may make up an answer to an authorship question. Its response is not provenance evidence. OpenAI Help: how to tell if text is AI-generated.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What provenance signals can tell you
OpenAI documents provenance signals for certain content generated by its systems, but cautions that these are not a general-purpose AI detector and do not identify content from every company’s models. A recognized signal may be relevant to the content and systems it covers; a missing or unrecognized signal does not show that the content was written by a person. OpenAI content-provenance guide.
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