Reasonable Expectations of Privacy and Novel Search Technologies: An Economic Approach
Bibliographic record
Abstract
The “reasonable expectation of privacy” test, which defines the scope of constitutional protection from governmental privacy intrusions in both the United States and Canada, is notoriously indeterminate. This indeterminacy stems in large measure from the tendency of judges to think of privacy in non-instrumentalist terms. This “moral” approach to privacy is normatively questionable, and it does a poor job of identifying the circumstances in which privacy should prevail over countervailing interests, such as the deterrence of crime.\nIn this paper, I develop an alternative, economically-informed approach to the reasonable expectation of privacy test. In contrast to the moral approach, which treats privacy as a fundamental right, the economic approach views it as a (normatively neutral) aspect of self-interest: the desire to conceal and control potentially damaging personal information. On this view, privacy should not be protected when its primary effect is to impede the optimal deterrence of crime. Legal protections against governmental surveillance, however, may in other cases enhance social welfare by encouraging productive transactions, diminishing the costs of non-legal privacy barriers, and limiting suboptimal policing practices, including discriminatory profiling and the enforcement of inefficient criminal prohibitions. Economics and public choice theory can also help to minimize decision-making error by predicting which legal actors – police, legislatures, or courts – are best placed to make optimal trade-offs between privacy and crime control.\nI first describe the United States and Canadian supreme courts’ reasonable expectation of privacy jurisprudence and canvass its chief inadequacy: the vagueness of the “public exposure” and “intimacy” doctrines that the courts have used to decide whether to regulate novel search technologies. I then outline the economic approach to the reasonable expectation of privacy test. Next, I apply this approach to two technologically advanced search tools: infrared imaging and location tracking. This analysis suggests that courts should recognize a reasonable expectation of privacy in the latter case, but not the former.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".