SHIELD AI KNOWLEDGE CENTER
AI in Criminal Investigations
A practical, legal, and operational guide to using artificial intelligence across the investigative lifecycle while preserving constitutional safeguards, evidentiary integrity, human judgment, and public trust.
Interactive investigation map
Where AI May Enter an Investigation
Select a stage to see possible uses, required human decisions, documentation points, and recurring legal questions.
Technology explorer
Investigative AI Technologies
Filter by investigative purpose or primary risk area. Each card distinguishes a possible capability from the judgment and corroboration still required of investigators.
Constitutional and evidentiary overlay
Legal Issues by Investigative Use
The relevant issue often depends less on the AI label than on how information was collected, what the system did with it, and how the output was later used.
Scenario-based training
Investigation Decision Lab
Work through a realistic investigative decision. The feedback identifies considerations to document; it does not declare a single legally correct outcome.
Operational risk framework
Eight Risks That Require Deliberate Controls
The relative significance of each risk changes by tool, data source, configuration, investigative purpose, and jurisdiction.
Unsupported Output
Generated statements, labels, or inferences may exceed what the source material supports.
Misidentification
A similarity result or algorithmic match may be treated as identification rather than an investigative lead.
Source Loss
Investigators may retain the output but fail to preserve the source, prompt, model version, or intermediate record.
Automation Bias
Reviewers may defer to polished output even where it conflicts with direct evidence.
Scope Expansion
A tool acquired for one purpose may later be applied to broader data or less serious investigations.
Disparate Performance
Performance may vary across lighting, demographics, language, accents, environments, or data quality.
Discovery Failure
Agencies may not identify what output, metadata, audit history, or vendor-held information must be preserved or disclosed.
Vendor Dependence
Model changes, proprietary methods, retention limits, and contract terms may affect reproducibility and testimony.
Agency self-assessment
AI Investigation Readiness Check
Select the controls your agency currently has in place. The result is an organizational prompt—not a certification or legal conclusion.
Beginning: inventory current uses and assign ownership.
Implementation resources
Agency Investigation Toolkit
These modules can become separate interactive tools or downloadable agency forms as the knowledge center expands.
AI Use Documentation Form
- Tool, user, date, and purpose
- Source material and legal authority
- Prompt or settings
- Output retained
- Corroboration and disposition
Lead Validation Worksheet
- Separate lead from evidence
- Identify independent corroboration
- Record conflicting information
- Document human judgment
- Identify warrant-affidavit use
Prosecutor Disclosure Checklist
- AI involvement identified
- Source and output preserved
- Material edits documented
- Known limitations disclosed
- Vendor records evaluated
Search-Warrant Issue Finder
- Data source and custodian
- Time and geographic scope
- Particularity and minimization
- Derived or transformed data
- Return, retention, and sealing
Supervisor Audit Form
- Authorized use confirmed
- Output independently reviewed
- Bias or accuracy issue checked
- Records retained
- Training issue identified
Vendor Evaluation Matrix
- Performance and validation
- Auditability and export
- Security and data use
- Model-change controls
- Contract and testimony support
Core operating model