Thank you for your question.
My comments here really relate to the increasing need for good-quality data to produce policy that's effective. We've seen that, whether it comes from open-source data, industry data, or data generated through specific research, which many of us who are academics are involved in, this data is increasingly necessary to build public policy.
You will recall that we had similar arguments around good-quality datasets a decade ago, in the arguments about the long-form census. On that occasion, while there were arguments being made about privacy and so on, most of us in the academic world were actually on the other side of the debate and arguing in favour of the long-form census because it provided important data that allowed us to make effective social policy. I think that's the importance of this sort of dataset.
As David Lyon said earlier, it does not mean there are no risks. It does not mean that data can just be used in any way that a government sees fit to use it. It does not mean that government does not have to account for data and how it is used or provide evidence of consent, as Dr. Parsons has said. I think those things are all very important.
The final thing I didn't get to in my opening statement, which is absolutely vital, is to expand on what Professor Lyon said about group harms. One key thing about large datasets is that hidden within these datasets are existing forms of bias and prejudice.
I'll give you an example. Say, in Telus's “data for good”—this is just made up, by the way—it was found that people in a particular suburb of Toronto were travelling further distances more often than other people in Toronto. You could easily assume from this data that these people were spreading the virus or were disobeying government instructions on travel. In fact, if you look into this particular suburb, you find it's a low-income place, largely Black and of ethnic minority. You have in this area people who have to travel to get to warehousing jobs or work in the gig economy, and the reason they're mobile and moving more often is precisely because they're under-privileged. Therefore, to stigmatize these people or to blame them for the virus spread would be to misread the social facts on the ground.
That's just one notional example, but it's very important to be able to understand not just the data as facts but the data in its social context. That's what I think is really vital when we talk about—