Bibliographic record
Abstract
Abstract Can robots or AIs operating independently of human intervention or oversight diminish our privacy? There are two equal and opposite reactions to this issue. On the robot side, machines are starting to outperform human experts in an increasing array of narrow tasks, including driving, surgery, and medical diagnostics. This is fueling a growing optimism that robots and AIs will exceed humans more generally and spectacularly; some think, to the point where we will have to consider their moral and legal status. On the privacy side, one sees the very opposite: robots and AIs are, in a legal sense, nothing . The received view is that since robots and AIs are neither sentient nor capable of human-level cognition, they are of no consequence to privacy law. This article argues that robots and AIs operating independently of human intervention can and, in some cases, already do diminish our privacy. Epistemic privacy offers a useful analytic framework for understanding the kind of cognizance that gives rise to diminished privacy. Because machines can actuate on the basis of the beliefs they form in ways that affect people’s life chances and opportunities, I argue that they demonstrate the kind of cognizance that definitively implicates privacy. Consequently, I conclude that legal theory and doctrine will have to expand their understanding of privacy relationships to include robots and AIs that meet these epistemic conditions. An increasing number of machines possess epistemic qualities that force us to rethink our understanding of privacy relationships with robots and AIs .
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".