14 H.Q. Summer 2026 2. CENTRALIZED INVENTORY AND VISIBILITY Police services should maintain an enterprise inventory of AI-enabled technologies. This includes: • Systems currently in production; • Embedded AI features within devices or platforms; • Vendor-managed or cloud-based AI services; and • Pilot, trial or proof-of- concept initiatives. An accurate inventory provides the foundation for risk manage- ment, auditability, transparency and informed decision-making. Without visibility, meaningful governance is not possible. 3. RISK ASSESSMENT AND INTENDED-USE ANALYSIS Not all AI poses the same level of risk. Governance frameworks should incor- porate structured risk assessments that evaluate: • Intended operational purpose and context; • Data sensitivity and data flows; • Degree of automation versus human oversight; • Potential for bias, error or unintended consequences; and • Legal, reputational and commun- ity trust impacts. Risk assessments should be com- pleted prior to approval and revisited whenever systems change, new features are enabled or use cases expand. This ensures governance remains aligned with real-world operational use. 4. FORMAL APPROVAL AND DOCUMENTATION PROCESSES AI-enabled technologies should follow defined approval pathways involving IT, privacy, legal and operational stakeholders. Documentation should clearly articulate: • What the AI does and does not do; • How outputs are generated and used; • Where human review or inter- vention occurs; and • How outputs are validated, challenged or overridden. This documentation supports internal accountability and is essen- tial for responding to audits, court proceedings, public inquiries and oversight bodies. MONITORING, AUDITABILITY AND ACCOUNTABILITY Trustworthy AI does not end at deployment. Ongoing oversight is essential to ensure systems continue to operate as intended and within approved parameters. Effective governance models include: • Scheduled reviews of AI-enabled systems and features; • Audits of mechanisms to assess compliance with approved use; • Monitoring for model drift, bias or unexpected behaviour; and • Clear ownership and accountability for each AI-enabled system. An important pint that must be emphasized is that accountability must remain human-centred. AI may support or inform deci- sion-making, but responsibility for outcomes must always rest with sworn members, supervisors and organizational leadership. WORKFORCE AND ECONOMIC IMPACTS AI adoption has real implications for the police environment. While AI can significantly improve efficiency by reducing administrative burden, accelerating evidence processing and enhancing situational awareness, it also introduces new risks if members are not properly trained. AI LITERACY AND TRAINING Operationalizing trustworthy AI requires investment in AI literacy across the organization. Members need to understand: • What AI is and where it is used; COMMON GOVERNANCE PITFALLS TO AVOID As police services scale AI use, several common pitfalls emerge: • Treating AI as a one-time project rather than an evolving capability; • Over-reliance on vendor assurances without independent assessment; • Inconsistent governance across units or technologies; • Lack of documentation, making systems difficult to defend or audit; and • Assuming AI is neutral, rather than actively monitoring for bias and error. Avoiding these pitfalls requires intentional design, sustained leadership attention and cross- functional collaboration.
View this content as a flipbook by clicking here.