Beyond the Hype: Operationalizing trustworthy AI in Ontario policingBy Christine Robson, Durham Regional Police ServiceArtificial Intelligence (AI) is no longer experimental in policing - it is operational, embedded and increasingly inseparable from modern law enforcement infrastructure.Across Ontario, AI capabilities exist not only in investigative or analytical software, but directly within frontline hardware and core platforms, including body-worn cameras, in-car video systems, automatic licence plate recognition (ALPR), mobile devices, radios, network infrastructure, records management systems and cybersecurity tools. In many cases, these capabilities are enabled by default through firmware updates, cloud subscriptions or vendor-managed services, often without the same visibility, scrutiny or governance traditionally applied to standalone applications.For Ontario police leaders, the central question is no longer whether AI will be used, but how it can be governed responsibly, transparently and at scale. This requires a deliberate shift away from viewing AI as a discrete or emerging technology and toward managing it as an enterprise capability – one that spans applications, devices, data pipelines, vendors, procurement practices and people. Trustworthy AI in policing is therefore not a technical problem alone; it is an organizational, legal and governance challenge.This article examines how Ontario police services can operationalize trustworthy AI through practical, enterprise-wide governance models. It explores how governance can be applied consistently across AI-enabled technologies regardless of form factor, and outlines approaches to policy alignment, risk assessment, approval and documentation, monitoring, accountability, workforce impacts and common pitfalls to avoid as AI becomes embedded across modern policing ecosystems.AI IS ALREADY EMBEDDED One of the most significant governance challenges facing police services today is that AI is no longer confined to clearly labeled “AI systems.” Modern policing technologies increasingly include AI-driven capabilities such as automated transcription, video and audio redaction, object and behaviour detection, pattern recognition, anomaly detection, threat scoring, predictive maintenance and adaptive cybersecurity controls. These capabilities are frequently delivered through:
  • Vendor-managed cloud platforms; 
  • Firmware and software updates; 
  • Embedded intelligence within devices; and 
  • Machine-learning-based security and monitoring tools.
Because these capabilities are often bundled into broader platforms, services may already be using AI in operational contexts without a consistent understanding of where it exists, how it functions, what data it processes or what operational decisions it influences. Treating AI governance as an application-level issue is no longer sufficient. Instead, governance must account for AI wherever it appears - whether in evidence management, infrastructure or security tooling.FROM POLICY TO PRACTICEOperationalizing trustworthy AI requires governance that applies consistently across the entire technology lifecycle – procurement, deployment, use, monitoring and retirement – regardless of whether AI is delivered through software, hardware or managed services. Effective governance recognizes that if an AI capability can influence operational decisions, evidence or member behaviour, it must be visible, documented and accountable.Key elements of an effective enterprise AI governance framework include the following.1. CLEAR POLICY ALIGNMENTAI governance must be anchored in existing legal, ethical and operational frameworks rather than treated as a separate or experimental domain. Effective AI policies should align with:
  • Privacy and access-to-information legislation; 
  • Records management and evidentiary standards; 
  • Professional standards and accountability frameworks; 
  • Cybersecurity and information security policies; and 
  • Procurement and vendor management practices.
Importantly, AI policies should apply equally to internally developed tools, commercial platforms and vendor-embedded capabilities. This ensures consistent expectations regardless of where or how AI is introduced.2. CENTRALIZED INVENTORY AND VISIBILITYPolice 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 management, auditability, transparency and informed decision-making. Without visibility, meaningful governance is not possible.3. RISK ASSESSMENT AND INTENDED-USE ANALYSISNot all AI poses the same level of risk. Governance frameworks should incorporate 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 community trust impacts.
Risk assessments should be completed 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 PROCESSESAI-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 intervention occurs; and
  • How outputs are validated, challenged or overridden
This documentation supports internal accountability and is essential for responding to audits, court proceedings, public inquiries and oversight bodies.MONITORING, AUDITABILITY AND ACCOUNTABILITYTrustworthy 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 decision-making, but responsibility for outcomes must always rest with sworn members, supervisors and organizational leadership.WORKFORCE AND ECONOMIC IMPACTSAI 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 TRAININGOperationalizing trustworthy AI requires investment in AI literacy across the organization. Members need to understand:
  • What AI is and where it is used;
  • The limitations and risks of AI-generated outputs; and 
  • How to appropriately rely on, question and document AI-assisted work.
Training should extend beyond technical staff to include frontline members, supervisors and command staff.AI-driven efficiencies should be planned deliberately. Time saved through automation must be reinvested thoughtfully – whether into investigative quality, community engagement, supervision or member wellness – rather than assumed to be automatically beneficial. Without intentional planning, efficiency gains can introduce new pressures, unrealistic performance expectations or unintended risk.LABOUR RELATIONS AND ROLE EVOLUTIONThe introduction of AI-enabled technologies has important implications for labour relations and the evolution of policing roles. While AI is often framed in terms of automation and efficiency, in policing it functions primarily as decision support rather than decision replacement. Governance frameworks must clearly reinforce that AI does not supplant professional judgment, discretion or accountability.As AI becomes embedded in operational workflows, supervisors retain responsibility for ensuring that AI-assisted outputs are appropriately relied upon, reviewed and documented.As AI capabilities mature, the nature of police work is likely to evolve incrementally rather than dramatically. Members may spend less time on repetitive administrative tasks – such as manual transcription, data entry or evidence sorting – and more time interpreting information, exercising judgment, engaging with communities and articulating decision-making.This shift places greater emphasis on analytical skills, critical thinking and the ability to clearly explain how information, including AI-assisted out- puts, informed operational actions.Supervisory and management roles will also evolve. Supervisors may be required to review AI-assisted outputs for reasonableness, ensure that appropriate human oversight is applied and intervene when technology begins to influence outcomes in unintended ways.At the command level, leaders must ensure that performance expectations, operational directives and productivity measures do not implicitly encourage over-reliance on automation or speed at the expense of investigative quality, fairness or officer discretion.From a labour relations perspective, transparency is essential. Police services should proactively engage associations and members when introducing or expanding AI-enabled capabilities. Clearly communicating what AI will and will not be used for – particularly in relation to performance monitoring, discipline or surveillance – helps mitigate mistrust and misinformation. Establishing clear boundaries around acceptable use, audit access, data retention and oversight supports confidence that AI will not be applied in ways that undermine collective agreements or workplace protections.Ultimately, successful AI adoption in policing depends on treating members as informed professionals and active participants in change. When governance, training and labour considerations are addressed together, AI can enhance operational effectiveness while preserving professional autonomy, public trust and workplace confidence.SCALABLE, PROVINCE-WIDE MODELSAI governance is not a challenge any single service should solve in isolation. There is a clear opportunity for Ontario-wide collaboration through shared standards, templates and best practices. Scalable models could include:
  • Members on the OACP AI Committee reports to the OACP Common Police Environment Group-Information and Technology Sub-Committee to assist with governance and process;
  • Common AI risk assessment frameworks; 
  • Shared definitions and taxonomy for AI-enabled technologies; 
  • Provincial guidance on transparency and disclosure; and 
  • Collaborative engagement with academic, legal and technical experts.
By aligning approaches, police services can reduce duplication, improve consistency and strengthen public trust.CONCLUSIONAI is no longer optional or peripheral in policing. It is embedded, operational and accelerating. Trustworthy AI will not be achieved through technology alone, but through disciplined governance, informed leadership and a commitment to transparency and accountability. Ontario police services that move beyond hype and operationalize AI responsibly will be better positioned to protect public trust, support their members and adapt confidently to a future where AI is inseparable from modern policing infrastructure.Christine Robson is a technology leader with more than 30 years of experience delivering secure, innovative IT solutions. Currently the IT Manager at Durham Regional Police Service, she oversees enterprise strategy, cybersecurity and 9-1-1 emergency systems. Beyond her technical expertise, Robson is a formally recognized thought leader in generative AI and data governance. A dedicated mentor and advocate for women in technology, she is known for her inclusive leadership and her ability to drive complex organizational change while building high-performance, collaborative teams.