Teresa Scassa - Blog

Note: This is the first in a series of posts that will look at the proposed amendments to Canada's Artificial Intelligence and Data Act, which is Part III of Bill C-27, currently before Parliament. The amendments are extensive and have only just been introduced. Please consider these assessments to be preliminary.

Canada’s Artificial Intelligence and Data Act (AIDA) (Part III of Bill C-27) has passed second reading and is currently before the INDU Committee for study. Early in this committee process, the Minister of Industry Philippe Champagne announced that his department was working on amendments to AIDA in response to considerable criticism. Those amendments have now been tabled for consideration by the committee.

One of the criticisms of the Bill was that it left almost all of its substance to be developed in regulations. It is unsurprising, then, that the amendments are almost as long as the original bill. While it is certainly the case that the amendments contain more detail than the original text, some of the additional length is attributable to new provisions intended to address generative AI systems. This highlights just how quickly things are moving in the AI space, as generative AI was not on anyone’s legislative radar when Bill C-27 was introduced in June 2022.

One of the criticisms of AIDA was the absence of any specific prior consultation before its appearance in Bill C-27. This, combined with its lack of substance on many issues, raised basic concerns about how it would apply and to what. For example, AIDA was to govern “high-impact” AI systems, but the definition of such systems was left to regulations. Concerns were also raised about oversight being largely in the hands of the Minister of Industry who is also responsible for supporting Canada’s AI sector.

The proposed amendments demonstrate that ISED has been listening to the feedback it has received since June 2022, just as it has been adapting to the challenges of generative AI, and engaging with its international partners on AI governance issues. The amendments, which include new definitions, more explicit obligations, and governance principles for generative AI, will make AIDA a better bill. They may be enough to garner sufficient support to pass it into law, something which the Minister describes as “pivotal”.

This is the first in a series of posts that will explore some of the changes proposed to AIDA – as well as some of the remaining issues. This post addresses some of the new definitions.

The proposed AIDA amendments propose a new definition of “artificial intelligence system” which would read: “a technological system that, using a model, makes inferences in order to generate output, including predictions, recommendations or decisions” (s. 2). This provides greater alignment with the OECD definition of an AI system (“An AI system is a machine-based system that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments. Different AI systems vary in their levels of autonomy and adaptiveness after deployment.”) It is an improvement over the previous definition, which was criticized for being too specific about the types of techniques used in AI. It is unclear, though, why the new AIDA definition does not include “content” as an output as is the case with the OECD definition. The AIDA definition is also supplemented by a separate definition for a “general-purpose system”, which is “an artificial intelligence system that is designed to be adapted for use, in many fields and for many purposes and activities, including fields, purposes and activities not contemplated during the system’s development” (s. 5(1)). There is a further definition for a “machine learning model”, which is “a digital representation of patterns identified in data through the automated processing of the data using an algorithm designed to enable the recognition or replication of those patterns”. A new s. 5(2) makes it clear that the definition of AI system includes general-purpose systems, and that general-purpose systems can also be high-impact. These new definitions reflect the major changes in both the technology and in the evolving regulatory context in the short time since AIDA was introduced. They also shape a new framework for obligations under the legislation.

The proposed amendments also contain a definition of “high-impact system”: “an artificial intelligence system of which at least one of the intended uses may reasonably be concluded to fall within a class of uses set out in the schedule”. (s. 5(1)). The previous version of AIDA left the articulation of “high impact” to future regulations. The schedule sets out a list of classes that describe certain uses. These are:

High-Impact Systems — Uses

1. The use of an artificial intelligence system in matters relating to determinations in respect of employment, including recruitment, referral, hiring, remuneration, promotion, training, apprenticeship, transfer or termination.

2. The use of an artificial intelligence system in matters relating to

(a) the determination of whether to provide services to an individual;

(b) the determination of the type or cost of services to be provided to an individual; or

(c) the prioritization of the services to be provided to individuals.

3. The use of an artificial intelligence system to process biometric information in matters relating to

(a) the identification of an individual, other than in cases in which the biometric information is processed with the individual’s consent to authenticate their identity; or

(b) the assessment of an individual’s behaviour or state of mind.

4. The use of an artificial intelligence system in matters relating to

(a) the moderation of content that is found on an online communications platform, including a search engine or social media service; or

(b) the prioritization of the presentation of such content.

5. The use of an artificial intelligence system in matters relating to health care or emergency services, excluding a use referred to in any of paragraphs (a) to (e) of the definition device in section 2 of the Food and Drugs Act that is in relation to humans.

6. The use of an artificial intelligence system by a court or administrative body in making a determination in respect of an individual who is a party to proceedings before the court or administrative body.

7. The use of an artificial intelligence system to assist a peace officer, as defined in section 2 of the Criminal Code, in the exercise and performance of their law enforcement powers, duties and functions.

(Note: the classes in this schedule will be the subject of the next blog post)

The list is not intended to be either closed or permanent. Under a proposed s. 36.1, the Governor in Council (GinC) can enact regulations amending the schedule by adding, modifying, or deleting a category of use. Any such decision by the GinC is to be guided by criteria set out in s. 36.1. These include the risks of adverse impact on “the economy or any other aspect of Canadian society and on individuals, including on individual’s health and safety and on their rights recognized in international human rights treaties to which Canada is a party”. The GinC must also consider the “severity and extent” of any adverse impacts, as well as the “social and economic circumstances of any individuals who may experience” such impacts. A final consideration is whether the uses in the category are adequately addressed under another Act of Parliament or of a provincial legislature.

The AIDA only applies to “high impact” systems, and since there is no screening or registration process, it is up to those who manage or make such systems available to identify them as such and to meet the obligations set out in the law. A proposed s. 14 would empower the AI and Data Commissioner to order a person who makes available or who manages an AI system to provide the Commissioner with their assessment of whether the system is a high impact system, a general purpose system (which can also be high impact), or a machine learning model intended to be incorporated into a high impact system.

My next post will look at the classes of “high-impact” AI as set out in the Schedule.