Teresa Scassa - Blog

In November 2025, Canada’s Treasury Board Secretariat made available a minimum viable product AI register, intended to form the basis for a consultation on what a register of AI in use in the federal public sector should look like. This dataset is not meant to represent in form or content what the final product will look like. But it is a starting point for a discussion. The consultation closes on March 31, 2026.

It is worth highlighting how significant the idea of a federal AI registry is. We are still in the early days of public sector AI, and there are relatively few precedents for official AI registers. That said, it is clear that this is a trend that is likely to grow. The Dutch government has a national AI register offering a public-facing searchable database that includes entries from federal and municipal governments. The UK has a register of “algorithmic tools” used in its public sector. Norway has what is described as an “overview” of AI projects in the public sector, which it cautions is a work in progress. France maintains an inventory of public sector algorithms, under the auspices of the Observatoire des algorithms publics. In the US, Executive Order 13960 requires federal agencies to create an inventory of their AI use cases, and guidance is provided on how to do this. While overview data is provided, each department maintains its own AI Use Case Inventory Library (see an example here). Canada’s decision to create a federal AI Register is an important commitment, and its consultation on what such a register should look like is also significant.

The consultation process is nourished by a dataset made available through Canada’s open data portal. Described as a minimum viable product, this is a pretty rough set of data compiled from different sources. It is really meant as a conversation starter – it provides a glimpse into what is already happening within the federal public sector when it comes to AI, and it prompts users to think about what data they might want to have, and how they might want to see it organized.

The current data set contains 409 separate entries, each with 23 data categories. These represent both French and English versions of the same categories. The categories include a unique identifier for each system, the system’s name and the government department or agency responsible for it. There is a short description of the system, information about primary users and about who developed the system. For procured systems, the name of the vendor is provided. The status of the system is indicated (e.g., in development, in production, or retired), as well as brief descriptions of system capabilities and data sources. Whether the system relies on personal data is also specified, as well as any relevant personal information banks. Whether users are notified of the use of the system is also indicated, and a short description is provided of the expected results of the system.

The AI register seems intended to serve two broad audiences. The first is users from within the federal government. By making its uses of AI systems more transparent internally, the government can avoid duplicative efforts, allow better collaboration across departments and agencies, and perhaps also share ideas for helpful uses of AI tools to streamline different processes. A second audience is the broader public. This audience can include researchers, journalists, academics, civil society organizations, lawyers, developers, and many others seeking to understand how and where the government is using AI systems. The diversity of potential users will impact both how the data are made available and what data points may be of interest.

The fact that the federal AI register seems intended for both internal and external audiences is important and should not be taken for granted. For example, Ontario’s Responsible Use of Artificial Intelligence Directive requires ministries and agencies to report on AI use cases and risk management, with ministries reporting to the Ministry of Public and Business Service Delivery and Procurement on an annual basis. However, this reporting requirement is internal and not public. The Directive only requires public disclosure of the use of an AI system where the public interacts directly with it or where the system is used to make a decision about a member of the public.

Currently Canada’s AI Register data is available in different formats, including CSV, JSON, TSV and XML These formats are useful for some types of users, but they are not particularly accessible for a broader public that might require a more user-friendly interface. Ideally, the AI Register should have a public facing site that makes it easy to search and find results offering straightforward information at a click. The UK’s Register provides an interesting example in this respect. For each algorithm there is a standardized list of information provided. It would be good to have a dashboard that provides visual representations of how and where AI is used in the federal public sector. This could include other overview representations of the data within the Register, but also, perhaps, information about the register itself (e.g, tracking the number of entries over time; tracking categories of uses, etc. For an example of a dashboard, see the one created by the Dutch Government as part of its AI Register). However, the more granular data should still be available through the open government portal as a downloadable dataset for those who wish to dig into it. This would be a useful resource for researchers, journalists, students, and others.

AI systems in use across the federal government may also have other data associated with them which it would be good to be able to access easily. For example, automated decision systems at the federal level are subject to the Directive on Automated Decision Making and are supposed to have gone through an algorithmic impact assessment (AIA). These assessments are meant to be available through the open government portal (and some are). Providing links to available AIA’s would be useful for those who want to know more about a particular system. Similarly, systems that use personal data will have gone through a privacy impact assessment, and many systems will also have gone through a Gender-based Plus assessment. Links to any publicly accessible evaluations would be useful, but even if these are not fully publicly available, the register could indicate whether the AI system has gone through such an evaluation, and when it might have been updated.

Other data points that could be considered might include whether there is human oversight and at what point in the process. In the current version of the Register, data sources are identified (e.g., certain categories of documents), but it might also be useful to know what specific data points are relied upon (this is something that is provided, for example, in the Dutch register).

Presumably AI systems in use in the public sector will be monitored and assessed, and data will be gathered on their performance. Are the systems reducing workload or backlogs and if so, by how much? Are they replacing humans? Saving money? Generating complaints? Are any reports, audits, and assessments publicly available? If so, where? When it comes to assessments and reports, it is not necessary for the AI register to be overburdened with too many data points. However, other relevant information that is proactively published should be easily findable.

Once TBS has decided what data should be in the register, it will need to provide a mechanism to gather this data and to ensure that it is harmonized across the federal public sector. This will likely require providing fillable forms in which terminology is carefully defined.

Generative AI and its use in the public sector will present some interesting challenges for the AI Register. Some uses of generative AI within departments or agencies are likely to be fairly ad hoc (as, for example, when AI is used to translate an email or document received that is in a language other than French or English). On the other hand, a deliberate choice to use genAI to translate such materials in a context in which they are frequently received, might require disclosure. Similarly, the ad hoc use of genAI to summarize reading material may not require disclosure, but a systematic approach to summarizing with genAI in administrative processes should require disclosure (and might require an algorithmic impact assessment). An example of this might be the systematic use of AI to summarize evidence or submissions to an agency or tribunal. Focusing on the nature/extent of use is one way of approaching this. Another might be to assess whether there is a public-facing dimension to the use of genAI. If it is used solely for internal administrative purposes, perhaps disclosure in the registry is less necessary than if it is used in a decision-making process, or if it is used in communications with the public. This latter way of approaching it could get complicated, since it may be difficult to determine which internal administrative uses end up having public facing dimensions. For example, genAI used in summarizing and report-drafting could have very public dimensions if that research shapes policy documents, white papers, consultation materials or other public-facing content. And, as reliance on agentic AI systems expands, it will also become necessary to think about how agentic AI use cases are recorded and documented within the register.

There may also be uses that the government decides should not be in the Register for reasons related to cybersecurity, national security or law enforcement practices, for example. Certainly, disclosing what AI systems are used to protect against cyberattacks or that are used in the national security context may be contrary to the public interest. Law enforcement is a trickier category, as there are some types of systems (e.g., predictive policing, facial recognition technology) for which transparency and accountability seem squarely in the public interest. (Note that the Dutch database contains 13 entries related to policing, including both FRT and predictive policing models.) Others (e.g., particular fraud detection algorithms) may require more circumspection.

A final point is to consider how often departments and agencies will be required to update their entries. Systems evolve and acquire new functionalities all the time. Sometimes modifications are significant enough to warrant new AIA’s or PIA’s. Whatever choices are made for the launch of Canada’s AI Register, the Register itself should be part of an iterative process subject to periodic reviews and updates, and open to user feedback.

Canada’s federal government has just released an early version of the AI Register it promised after its election earlier this year.

An AI Register is an important transparency tool – it will help researchers and the broader public understand what AI-enabled tools are in use in the federal public sector and provides basic information about them. The government also intends the register to be a resource for the public sector – allowing different departments and agencies to better see what others are doing so as to avoid duplication and to learn from each other.

The information accompanying the Register (which is published on Canada’s open government portal) indicates that this is a “Minimum Viable Product”. This means that it is “an early version with only basic features and content that is used to gather feedback.” It will be interesting to see how it develops over time.

One interesting aspect of the register is that it states that it was “assembled from existing sources of information, including Algorithmic Impact Assessments, Access to Information requests, responses to Parliamentary Questions, Personal Information Banks, and the GC Service Inventory.” Since it contains 409 entries at the time of writing, and since there are only a few dozen published Algorithmic Impact Assessments (AIAs), this suggests that the database was compiled largely using sources other than AIAs. The reference to access to information requests suggest that some of the data may have been gathered using the TAG Register Canada laboriously compiled by Joanna Redden and her team at the Western University. The sources for the TAG Register also included access to information requests and responses to questions by Members of Parliament. Prior to the development of the federal AI Register, the TAG Register was probably the most important source of information about public sector AI in Canada. The TAG Register is not made redundant by the new AI Register – it contains additional information about the systems derived from the source materials.

The federal AI Register sets out the name of each system and provides a description. It indicates who the primary users are, and which government organization is responsible for it. Other fields provide data about whether the system is designed in-house or is furnished by a vendor (and if so, which one). It also indicates whether the system is in development, in production, or retired. There is a brief description of the system’s capabilities, some information about the data sources used, and an indication of whether it uses personal data. The register also indicates whether users are given notice of use. There is a brief description of the expected outcomes of the system use.

All in all, it’s a good start, and clearly the developers of this database are open to feedback. (For example, I would like to see a link to the Algorithmic Impact Assessment under the Directive on Automated Decision-Making, if such an assessment has been carried out).

This is an important transparency initiative, and it will be a good source of data for researchers interested in public sector AI. It is also an interesting model that provincial governments might want to consider as they also roll out AI use across their public sectors.