The committee studied the challenges posed by artificial intelligence regulation. Appearing were Frédéric Gonzalo, a consultant in digital marketing and AI, and Vasiliki Bednar and Matthew da Mota from the Canadian SHIELD Institute for Public Policy.
Frédéric Gonzalo said regulatory uncertainty paralyzes small and medium-sized enterprises, which lack legal teams and need a simple, tiered framework that distinguishes low-risk uses like writing texts from high-impact systems. He recommended clear Canadian guidelines on consent, anonymization and data minimization tailored to small organizations, and noted that digital literacy remains a challenge, with employees using AI personally but rarely in structured work settings. He also raised concerns about the transformation of search engines into AI engines, which complicates digital discoverability for businesses.
Vasiliki Bednar argued that AI regulation cannot remain anchored only in privacy and consent frameworks, because AI is already shaping markets, culture and economic outcomes through live use cases like algorithmic pricing, AI-generated music and autonomous payment systems. She said the biggest structural weakness is Canada’s reluctance to intervene for fear of impeding innovation, and that trade agreements like CUSMA constrain the ability to mandate data residency or audit algorithms. She recommended regulating downstream power—how systems shape prices, wages, transactions and culture—and called for knowability, so consumers can tell when they are interacting with an AI system.
Matthew da Mota said AI poses a threat to epistemic sovereignty—the ability of a country to control its knowledge environment—because models are trained on opaque data and can be manipulated to skew narratives. He argued that regulation does not necessarily kill innovation, citing Canada’s nuclear sector as an example where strong regulation enabled success, and cautioned against using regulatory harmonization as a signal for deregulation. He suggested that attaching liability to licensed professionals, such as engineers, could help ensure accountability for AI deployment.
The committee also heard that algorithmic pricing enables personalized, discriminatory pricing that extracts value from consumers, with loyalty programs and apps inferring personal data like payday schedules. Witnesses disagreed on whether education alone can address AI-generated disinformation, with Bednar calling it an intentional deceit rather than an education failure, while Gonzalo noted that platforms rely on voluntary transparency measures. On productivity, Gonzalo said AI integration takes time and warned against a bidding war in AI investments, while Bednar cautioned against over-promising productivity gains from unproven assumptions. No procedural debate, motions or votes occurred during the meeting.
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