The Standing Senate Committee on Social Affairs, Science and Technology is one of several committees in the House and Senate conducting hearings on artificial intelligence. I appeared before the committee yesterday (my fourth appearance on the issue in recent months), but rather than reiterate previous testimony on privacy, copyright, and transparency, I focused on the big issue of the moment: bans on social media and AI chatbots for children. The committee had been hearing from many supportive witnesses who emphasized the risk of harm associated with AI. Indeed, one Senator asked the panel before mine to raise their hands if they supported a ban, and virtually all hands went up. I was unsure about how my comments would be received, but I found the Senators open to debate on the issue. A video of my opening remarks, together with the transcript, is posted below. A future Law Bytes podcast episode will delve into the discussion that followed.
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Government Has a Choice: Why an AI Chatbot Ban for Kids is an Even Worse Idea Than a Social Media Ban
The frenzy to ban kids from social media continues to grow with Culture Minister Marc Miller telling a House of Commons committee that the government has no choice but to act. Miller’s comments are consistent with the federal Liberal policy convention vote backing a minimum age of 16 and Manitoba Premier Wab Kinew announcing that his government will be the first in Canada to ban kids from both social media and AI chatbots. The problem, as I documented in detail last week, is that good intentions do not make for good policy. In this case, a social media ban is bad policy because it does not address the underlying problems with the platforms, evidence to date suggests it doesn’t work, and it creates its own harms. But the bad policy does not end there, as the possibility of extending that same framework to AI chatbots is now squarely on the table. This post examines the implications of a ban on kids’ use of AI chatbots, arguing that such an approach is even worse than a social media ban. To be clear, regulation of AI chatbots is needed, but a ban leaves the genuine concerns associated with AI chatbots largely untouched.
AI Without Canada: Why the Heritage Committee’s AI Report Could Lead to Less Canadian Content in the Training Data
When I appeared before the Standing Committee on Canadian Heritage last fall for its study on AI and the creative industries, I emphasized that the large language models and generative AI systems that are reshaping how people access information, culture, and entertainment are only as representative as the data on which they are trained. If Canadian works, perspectives, and cultural content are absent from those models, Canada risks disappearing in the AI-mediated world. The committee’s report, released this month, acknowledges this concern, but its lead recommendation risks making the situation worse.
Addressing the AI Policy Challenge: My Appearance before the Standing Senate Committee on Transport and Communications
Earlier this week, I appeared before the Standing Senate Committee on Transport and Communications as part of its study on AI regulation. This follows earlier appearances before the House of Commons Heritage and Industry committees on the same issue. The hearing led to robust exchanges with multiple Senators on the intersection of AI policy with issues such as privacy, copyright, online harms, and sovereignty. I plan to post clips from the hearing in a future Law Bytes podcast, but in the meantime, my opening statement provides a good sense of my views on AI regulation with respect to privacy, copyright, and the need for an AI Transparency Act. A video of the opening statement is embedded below, followed by the text.
Is Data De-Identification Dead?: Why the AI Privacy Risk Isn’t What It Learns, But What It Figures Out
In 1997, an MIT graduate student named Latanya Sweeney stunned the privacy world by matching publicly available voter rolls with hospital records stripped of names and addresses to identify the supposedly anonymous medical history of the then-governor of Massachusetts. Three years later, she expanded on that finding by demonstrating that 87 per cent of the U.S. population could be uniquely identified using just three data points: ZIP code, date of birth and gender.
My Globe and Mail op-ed notes that Ms. Sweeney’s work shaped privacy frameworks worldwide, which responded with de-identification standards designed to manage the risk by removing obvious identifiers, applying statistical tests and treating the resulting data as safe to use. Indeed, a core tenet of modern privacy regulation rests on the premise that de-identified data can be used, disclosed and commercialized without compromising individual privacy.
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