: “I’m sorry, Dave, I’m afraid I can’t do that” Part 2
Bibliographic record
Abstract
In the WYPT session at the Baltimore ACA meeting in 2023, I instigated a discussion on the use/abuse/future of AI generated text in publications (and elsewhere). One of the main take-aways from that talk was the prospect of AI detecting itself: that is, AI applications that can detect with some level of accuracy and precision whether a piece of text was generated by human or AI. Since then, there have been such routines developed and tested. One (of many) report did a fairly thorough analysis of the most common software: (W.H. Walters, “The Effectiveness of Software Designed to Detect AI-Generated Writing: A Comparison of 16 AI Text Detectors” https://doi.org/10.1515/opis-2022-0158 ) This talk will present some conclusions from that report and pose some questions on how to proceed: Can there be safeguards to distinguish AI text reliably? Can these contribute to defining potential legitimate uses of AI-generated text while still protecting copyright and IP? To what extent might we be able to use AI-generated text to prepare publications, reviews, grant proposals, etc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.095 | 0.065 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".