Bibliographic record
Abstract
Privacy remains one of the most recurrent concerns that people have about AI technologies. The meaning of the concept of “privacy” has proven to be fairly elusive. Accordingly, the concerns people have about privacy are often vague and ill-formed, which makes it correspondingly difficult to address these concerns, and to explain the ways in which AI technologies do or do not pose threats to people's interests. In this article, we draw attention to some important distinctions that are frequently overlooked, and spell out their implications for concerns about the threats that AI-related technology poses for privacy. We argue that, when people express concerns about privacy in relation to AI technologies, they are usually referring to security interests rather than interests in privacy per se. Nevertheless, we argue that focusing primarily on security interests misses the importance that interests in privacy per se have through their contribution to autonomy and the development of our identities. Improving insight about these issues can make it easier for the developers of AI technologies to provide explanations for users about what interests are and are not at stake through the use of AI systems.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".