General education, not legal advice. Published for informational purposes.
If your agency is looking at AI — and in 2026, most are — you've probably heard of the FASTER framework. It comes from the Government of Canada's Guide on the use of Generative AI, and it's become the de facto standard for thinking about responsible AI adoption in the public sector.
FASTER stands for Fair, Accountable, Secure, Transparent, Educated, and Relevant. Each principle maps to something concrete that a procurement and operational team should evaluate before bringing AI into investigative workflows. This guide breaks them down in plain language — what each one means, and what it should mean for your evaluation.
AI should not produce outcomes that discriminate against individuals or groups. In the investigative context, this means the AI should not introduce or amplify bias in the documentation it helps produce.
What to look for: Does the AI make decisions, or does it structure and draft from the investigator's own inputs? A system that drafts documentation from inputs is inherently lower-risk on fairness than a system that makes assessments or recommendations. Does the AI produce consistent output regardless of the subject's background, or are there patterns that suggest differential treatment?
The safest approach: the investigator remains the author. The AI structures and drafts — but the investigator reviews, edits, and owns every line. That's the model the FASTER framework's fairness principle points toward.
There must be clear lines of accountability for AI-assisted outputs. Someone must be able to answer the question: "Who produced this, and who approved it?"
What to look for: Does the platform maintain an audit trail that links every output to the investigator who reviewed and approved it? Can you trace every sentence back to its source in the evidence? Accountability is not just a policy statement — it's a technical capability. The platform should make it easy to demonstrate who did what, and when.
For investigative work, this is non-negotiable. If a report ends up in court — or in front of a tribunal, regulator, or internal review board — the author and the approval chain must be unambiguous.
AI systems must protect data from unauthorized access, modification, or exfiltration. In the law enforcement context, this goes beyond standard IT security to include sovereignty considerations.
What to look for: Where is the data? Where does the processing happen? Does the AI platform make outbound API calls to services outside your perimeter? If the AI model is hosted externally, what data travels to it?
The strongest security posture for investigative AI is on-premises deployment — all processing happens inside your network, and no data leaves your environment. If the platform uses cloud infrastructure, ensure it is Canadian-sovereign cloud with contractual commitments that match your security requirements.
Users and stakeholders should understand how the AI works, what it does, and what its limitations are. Transparency means the system's capabilities and boundaries are clearly communicated.
What to look for: Does the vendor clearly state what the AI does and does not do? Can the investigator see how the output was produced? Is the source-to-output chain visible and verifiable?
A transparent system doesn't produce "black box" outputs. Every claim in the output should be traceable to the evidence it's based on. This isn't just good practice — it's essential for disclosure and for defending the output in any formal proceeding.
Users must be trained — not just on how to use the tool, but on its limitations, its appropriate use cases, and the importance of human review.
What to look for: Does the vendor provide training that goes beyond button-clicking to cover the legal and operational context? Do investigators understand that they — not the AI — are responsible for the output? Does training emphasize that AI does not replace judgment, professional standards, or the investigator's obligation to verify?
The "educated" principle is a direct response to the risk of over-reliance. The best AI tool in the world is dangerous if the user treats it as an authority rather than a drafting assistant.
AI should be deployed to solve a real, defined problem — not deployed for its own sake. The use case should be clear, the scope should be bounded, and the deployment should be proportionate to the problem.
What to look for: Is the scope of the AI clear and narrow? Does the vendor make claims that the AI will "transform" or "revolutionize" everything, or do they articulate what specific task it accelerates?
The most defensible AI deployments are the narrowest ones. An AI that drafts narrative documents from the investigator's inputs, within a defined scope, with human review at every stage — that's a relevant deployment. An AI that claims to assess credibility or recommend outcomes is neither relevant nor responsible for investigative work.
The FASTER framework isn't a checklist you complete and file away. It's a lens you use during procurement and deployment to evaluate whether an AI tool is responsible, defensible, and appropriate for your agency's work.
When you evaluate a platform against FASTER, the through-line is clear: the investigator must remain in control. The AI should support — not supplant — professional judgment. Every output should be reviewable, traceable, and attributable to a human author. And the technology should operate within a clear, narrow scope that aligns with the principles.
If a platform can't demonstrate alignment with these principles — in writing, and in its architecture — it's not ready for investigative work.
This article is general education, not legal or procurement advice. For guidance on AI adoption in your agency, consult the Government of Canada's Directive on Automated Decision-Making and your agency's legal and privacy advisors.
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