Triage Evidence

Automated Prioritization: Could analyze the initial evidence and suggest which items need urgent review
Pattern Recognition: Could identify critical evidence based on case context
Risk Assessment Algorithms: Could evaluate the potential importance of evidence
Real-Time Guidance: Could adjust priorities as more data is processed
Predictive Models: Could inform investigators which evidence is likely most probative

Policies and Procedures: Establish triage guidelines for AI use
Accountability Structures: Assign review roles to senior staff
Training and Education: Ensure teams understand AI triage tools
Documentation Standards: Log AI decisions and human overrides
Compliance Checks: Routine verification of prioritization protocols

Risk Identification:

Technical - Wrong prioritization
Operational - Over-reliance
Ethical - Bias

Risk Sources: Quality of input data
Criminal Justice Partner Analysis: Investigators, lab technicians, quality assurance staff
Risk Documentation: Triage reports and logs

Performance Metrics: Correct prioritization rate
Assessment Tools: Benchmark tests against manual triage
Monitoring Techniques: Automated checks on priority lists
Feedback Loops: User feedback for accuracy
Validation Processes: Periodic expert review

Mitigation Strategies: Diverse data sets for training
Incident Response Plan: Review and adjust if misprioritization occurs
Adaptation and Continuous Improvement: Continuous updates with case results