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Tag: algorithmic pricing

  • Joint Submission to the People’s Consultation on AI

    Joint Submission to the People’s Consultation on AI

    On March 23, 2026, we submitted a report to the People’s Consultation on AI based on a hybrid event hosted jointly with the Tech Workers Coalition Canada on March 9, 2026.

    Our submission is below, followed by participants’ concluding demands:

    PCAI_Option2_TWCandTFD_20260309
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    The People’s Demands to the Government of Canada

    We asked the participants to grapple with what their demands are for how the Government of Canada can reconsider their National AI Strategy.

    • Co-develop regulations with communities: Currently, AI is developed and concentrated among a small number of market players that are built for a global optimum at the expense of optimizing for local community needs. There is an opportunity to have more local community input to shape rules on how this technology is produced and deployed.
    • Develop AI as publicly-owned infrastructure: Much in the way that utilities are often publicly-owned, we can institutionally entrench AI as a public good by having publicly-owned AI that is democratically controlled.
    • Ban AI from being deployed for surveillance: There are serious concerns with how AI can be used to surveil workers in the workplace and across society in general. With privacy being critical, the people demand
      that AI not be used for surveillance purposes.
    • Stronger labour protections for workers: There are many workers who will be impacted by this technology. The people believe that any claims of productivity gains will accrue to investors at the expense of workers. The people support the idea of a bill of rights for specific workers involved in the production of AI, such as data workers. The people also support collective bargaining agreements that would govern the use of AI in the workplace.
    • Regulatory bodies must protect themselves from regulatory capture by big tech: With the far-reaching impacts AI has had, the people demand that AI be regulated. Such demands include: monitoring how AI is deployed, requiring safety audits of AI models, and co-developing a bill of AI safety directly with
      communities. It is critical that workers and representatives from the community be directly involved in the governance of these bodies, and that they do not become captured by industry.
    • Enforce existing laws: There are many existing laws that are not being enforced that enable the proliferation of AI, such as privacy legislation, intellectual property legislation and competition legislation. The people demand that these existing laws be enforced.

  • Letter to Competition Bureau on Algorithmic Pricing

    In response to the Competition Bureau Canada’s call for feedback on algorithmic pricing, we submitted the following letter.

    See original PDF here.

    August 3, 2025

    Re: Algorithmic Pricing and Competition

    Algorithmic Pricing Needs Greater Transparency and Guardrails

    Dear members of the Competition Bureau,

    We are Technologists for Democracy, a grassroots advocacy organization based in Toronto. Our position largely concerns the effects of algorithmic pricing from the consumer perspective.

    Most consumers – 68% – feel that dynamic pricing unfairly takes advantage of them.1 This is not necessarily because of an inherent unfairness of dynamic pricing, but because most companies operate dynamic pricing through a purely profit-oriented lens focused on the short term. There is little thought given to transparency, company trust, or consumer well-being.

    Consumers are particularly vulnerable to downstream effects of algorithmic pricing, especially in markets of essential goods, markets with high barriers to entry and effective monopolies. Without reasonable alternatives, consumers become captive to price increases. Low-income consumers are particularly affected, due to any given purchase constituting a larger portion of their income. Housing is one such essential market – we welcome the Competition Bureau’s current probe into the use of algorithmic pricing for setting rental prices, and would recommend prohibiting the use of algorithmic pricing for housing (recommendation #6 below).

    In addition, we believe algorithmic pricing can lead to market inefficiencies. When companies offer personalized dynamic pricing, with different prices for each individual consumer, it becomes difficult for consumers to compare and recommend prices between competitors, and difficult for competitors to efficiently set prices according to the market. We see personalized dynamic pricing as a dangerous opportunity for larger entities to unfairly exploit their market dominance by reducing the information available to their competitors.

    To limit the harmful downstream outcomes of algorithmic pricing, our recommendations focus on improving transparency and guardrails surrounding algorithmic pricing. In the same way that nutrition facts labels and ingredient lists allow consumers to make informed decisions before purchasing food, labels on algorithmic pricing would allow consumers to make informed decisions before purchasing digital and digitally-enhanced products and services. 

    To improve transparency, we support regulation and enforcement which would require that companies clearly disclose:

    1. Whether or not algorithmic pricing is in effect for a given product or service.
    2. If algorithmic pricing is in effect, whether the pricing model was developed in-house or is outsourced to a third party.
    3. If algorithmic pricing is in effect, whether or not AI/machine learning is used for algorithmic pricing, as opposed to a rule-based model.
    4. If algorithmic pricing is in effect, what data is inputted into the pricing model. For example:
      1. Consumer data such as location, credit score or demographic profile,
      2. Inferred data such as consumer emotional state,
      3. Internal data such as sales counts,
      4. External data such as competitor prices or current weather,
      5. Etc.

    To ensure enforcement of policies, we support: 

    1. Establishing team(s) and process(es) to handle complaints and appeals for policies related to recommendations in this letter.

    In addition to educational regulation, we also support investigations as to the feasibility of:

    1. Prohibiting personalized, dynamic algorithmic pricing from being used in certain market sectors such as those of essential goods and services (including food, housing, and medication).
    2. Regulation of prices such as through a Maximum Retail Price policy, effective in countries such as India.

    We believe that these suggestions will:

    1. Improve consumer trust of algorithmic pricing.
    2. Inform consumers as to what personal data is involved in making a purchase.
    3. Lower the competitive barrier to entering markets already populated with algorithmic pricing models, while still allowing companies to maintain secrecy of the inner workings of proprietary pricing models.
    4. Reduce possible harms on consumers and competitors alike by preventing predatory pricing.
    5. Ensure equal opportunity of access to essential goods and services for consumers.

    We urge the Competition Bureau to increase transparency and implement guardrails on the use of algorithmic pricing.

    Sincerely,

    Khasir Hean
    [email removed for privacy]

    Henry Wilkinson
    [email removed for privacy]

    Jenny Zhang
    [email removed for privacy]

    Cole Anthony Capilongo 
    [email removed for privacy]

    Technologists for Democracy
    techfordemocracy.ca

    1. Gartner Marketing Survey Finds 68% of Consumers Report They Feel Taken Advantage of When Brands Use Dynamic Pricing. December 16, 2024. https://www.gartner.com/en/newsroom/press-releases/2024-12-16-gartner-marketing-survey-finds-68-percent-of-consumers-report-they-feel-taken-advantage-of-when-brands-use-dynamic-pricing ↩︎