Likelihood Is Not Authorship: Can We Reliably Measure Human Contribution in AI-Assisted Writing?
AI-text detection generally asks whether a machine wrote a text.
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Key points
- As AI becomes an increasingly integral and accepted part of writing, the more relevant question is no longer whether AI was used, but how much of the text reflects human contribution.
- We show that this measure can be easily manipulated.
- We introduce SPECIFICITY PADDING that simply asks the model to copy a few random IDs from the prompt, and that alone makes a title-only request look more human than one that supplies the article's key ideas.
- The same articles can average 85% human or 15% human depending only on which prompt is on record, and even with no prompt at all, the output-only variant rewards the random strings themselves.
Sources (1)
- [1]Likelihood Is Not Authorship: Can We Reliably Measure Human Contribution in AI-Assisted Writing?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 08:18 AM
AI-text detection generally asks whether a machine wrote a text.
As AI becomes an increasingly integral and accepted part of writing, the more relevant question is no longer whether AI was used, but how much of the text reflects human contribution.
Extractive summary: sentences quoted from the sources.
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