Ethical and legal considerations of artificial intelligence applications in psychiatric violence risk assessment: A scoping review protocol
Bibliographic record
Abstract
Violence risk assessment is a critical component of psychiatric practice, with significant clinical, ethical, and legal implications. Psychiatric patients at high risk of violence often face interventions including restraints, intramuscular injections, and involuntary hospitalization. Agitated and aggressive behaviours from patients have been linked to high hospital costs due to increased length of stay, readmissions, increased medication use, staff injury, and need for high acuity monitoring. Traditional risk assessment tools can be time intensive and have poor generalizability to civil populations. Recent advances in artificial intelligence (AI) have the potential for enhancing the precision of violence risk assessments. Although AI can address the technical issues of risk assessment, its implementation will raise new ethical and legal challenges. In psychiatry, AI-assisted violence risk assessment intersects with mental health law, particularly criteria for preventive detention and the ethical boundaries of AI-driven decisions. There have been some early concerns about racial bias, lack of transparency, accountability, and disruption to current practices in psychiatric care. To our knowledge, there have been no efforts to synthesize the ethical and legal implications for this particular use case. To address these gaps, we conducted a scoping review to map the literature on the ethical and legal considerations of AI in violence risk assessment in acute psychiatry.
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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 | 0.078 | 0.100 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.025 | 0.016 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.004 |
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".