Exceptions: The Criminal Law's Illogical Approach to HIV-Related Aggravated Assaults
Bibliographic record
Abstract
This Article identifies logical and due process errors in HIV-related aggravated assault cases, which usually involve an HIV-positive individual having unprotected sex without disclosing his or her HIV status. While this behavior should not be encouraged, this Article suggests that punishing this conduct through a charge of aggravated assault - which requires a showing that the defendant’s actions were a means likely to cause grievous bodily harm or death - is fraught with fallacies in reasoning and runs afoul of due process. Specifically, some courts use the "rule of thumb" that HIV can possibly be transmitted through bodily fluids as sufficient evidence for finding that a particular HIV-positive defendant who had unprotected sex did so in a manner likely to cause substantial harm. This leads to two due process errors: (1) the conflation of what is theoretically possible for what is likely, and (2) the use of data about a hypothetical, average, HIV-positive individual as proof of the effects of a particular HIV-positive individual’s behavior. By relying on the rule of thumb that HIV can be transmitted through bodily fluids rather than investigating the unique features of the particular defendant on trial, these jurisdictions violate the Due Process Clause’s requirement of "personal guilt." Aristotle’s "Fallacy of Accident" is then committed when this generalization is applied to an HIV-positive defendant whose viral load is undetectable, making him an exception to the general rule. After explaining these concepts, this Article identifies various cases from the states and the military that commit these errors. These cases are then compared to similar aggravated assault cases from Canada that do not make the same mistakes and use the kind of particularized proof that is required by both common logic and the Due Process Clause.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".