Position paper: should machine translation be labelled as AI-generated content?
Bibliographic record
Abstract
In September 2023, the Government of Canada issued a ‘Guide on the Use of Generative AI’ with recommendations for Canadian government institutions and their employees. As other similar documents published by various organizations in recent years, this document makes recommendations regarding transparency, stating that whenever generative AI is used to produce content, the reader should be informed that “messages addressed to them are generated by AI”. While this guide does not address specifically the case of machine translation, it does mention translation as a potential application of generative AI. Therefore, one question that naturally arises is: Should machine-translated texts be explicitly labelled as AI-generated content wherever they are used? In this position paper, we examine this question in detail, with the goal of proposing clear guidelines specifically regarding MT, not only for government institutions, but for anyone using MT technology to produce new versions of a text. Our main conclusion is that machine-translated text is indeed AI-generated content. As such, it should be explicitly marked everywhere it is used. We make recommendations as to what form this labelling might take. We also examine under what conditions labelling can be removed or omitted.
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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.012 |
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".