Industry committee  There can always be a tougher penalty system. However, as I said earlier, these systems aren't foolproof. The flawed nature of these artificial intelligence systems must be taken into account. A company may comply with all the processes, but in the end, the results may not be consistent or expected.

Industry committee  I think that the bill, as it stands today, doesn't go far enough to regulate the black box. That's really the issue. You're asking whether data use should be regulated. I think that it should. As you said a number of times, I think that the Privacy Commissioner can play a major role in raising awareness.

Industry committee  [Inaudible—Editor]

Industry committee  I agree with Mr. Gambs. I think political parties should be covered.

Industry committee  Allow me to answer you in French, in the same vein as Mr. Gambs. It is important to have the ability to increase control over the use of personal data in artificial intelligence systems. In my opinion, instead of looking at AI learning systems—and there are a number of them—the only process that would be important to examine further is the data we are going to learn about.

Industry committee  It depends. They are not industry criteria and Mr. Gambs can correct me if I am wrong. The success rate is used to evaluate the systems. If you are building a new system, you sometimes compare its success rate to the one for other systems or other algorithms. You are trying to create the best learning system possible.

Industry committee  I can't propose something that could be changed. I'm saying that you should really compare the system's ability to behave well with the risk that it may make mistakes. With the artificial intelligence systems available today, I think you have to allow for a margin of error because, even though the systems have a very high degree of reliability, they aren't 100% reliable.

Industry committee  No.

Industry committee  I am from Université Laval.

Industry committee  I will be happy to answer your question. I want to make sure I understood it. Let's talk about anonymous data. Assume that for the machine's learning, I do not use a person's name, or their race or origin, or social insurance number...

Industry committee  Learning is when we give a machine a certain amount of data. The learning may be supervised or not, and I am going to limit my remarks to those two types of learning. In the case of supervised learning, the machine is given a body of data that will be identified. For example, you say that Sehl Mellouli is a professor.

Industry committee  That's right. I always tell my students that if their system gives them results that are 100% correct, there is a problem. This is a computer program that is learning. When you learn from hundreds of thousands of pieces of data, you cannot be certain that all the data being used for learning is right.

Industry committee  Thank you for the invitation and for this opportunity to speak about the artificial intelligence bill and its application to data. I am not going to reiterate some of the things that Mr. Gambs discussed earlier. However, I would like to come back to certain things in the bill that are not entirely clear, in my opinion, or that should be clarified, particularly when we are talking about biased output.