Artificial Intelligence and Machine Learning at the Intersection of Privacy and Archives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As records are increasingly born digital – and thus, at least ostensibly, potentially much more accessible – archivists find themselves struggling to enable general access while providing appropriate privacy protections for the torrent of records being transferred to their care. In this article, the authors report the results of an integrative literature review study, examining the intersection of AI, archives, and privacy in terms of how archives are currently coping with these challenges and what role(s) AI might play in addressing privacy in archival records. The study revealed three major themes: 1) the challenges of – and possibilities beyond – defining “privacy” and “AI”; 2) the need for context-sensitive ways to manage privacy and access decisions; and 3) the lack of adequate “success measures” for ensuring the actual fitness for purpose of privacy AI solutions in the archival context.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.027 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it