Bibliographic record
Abstract
Abstract More and more, we face AI-based products and services. Using these services often requires our explicit consent, e.g., by agreeing to the services’ Terms and Conditions clause. Current advances introduce the ability of AI to evolve and change its own modus operandi over time in such a way that we cannot know, at the moment of consent, what it is in the future to which we are now agreeing. Therefore, informed consent is impossible regarding certain kinds of AI. Call this the problem of radical ignorance. Interestingly, radical ignorance exists in consent contexts other than AI, where it seems that individuals can provide informed consent. The article argues that radical ignorance can undermine informed consent in some contexts but not others because, under certain institutional, autonomy-protecting conditions, consent can be valid without being (perfectly) informed. By understanding these institutional conditions, we can formulate practical solutions to foster valid, albeit imperfectly informed consent across various decision contexts and within different institutions.
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 | 0.078 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.121 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".