This bill amends The Consumer Protection and Business Practices Act to prohibit suppliers—including online retailers, online distributors and systems that use electronic shelf labelling—from charging consumers a higher price at point of sale because an algorithm individualized the price. It adds new definitions, treats the use of personalized algorithmic pricing as a material fact, requires express consumer consent for price surcharges in online transactions, and declares certain algorithm-driven price differentials an unfair business practice.
For businesses and compliance officers, the law reshapes how dynamic and personalized pricing can be used in Saskatchewan: it constrains individualized price increases, forces clearer disclosure and consent mechanics, and exposes sellers and platforms to enforcement even when no sale is completed. Regulators gain explicit authority to treat algorithmic practices themselves as unfair conduct, while prosecutors' timelines are tightened to a two-year clock measured from the director's knowledge of evidence.
At a Glance
What It Does
The bill defines 'personalized algorithmic pricing' and forbids demanding a higher price at point of sale based on that pricing when prices are displayed via electronic shelf labelling, or for online retailers/distributors to use such pricing to increase amounts demanded. It makes use of such pricing a 'material fact' and requires prominent disclosure plus clear overt consent for online price differentials.
Who It Affects
Online retailers, marketplace operators, delivery-focused online distributors, providers of electronic shelf labelling systems, pricing-engine vendors, and data analytics firms that supply or operate algorithms used to set individualized prices.
Why It Matters
The bill erects a provincially specific barrier to individualized, data-driven price discrimination and shifts legal exposure from only completed transactions to algorithmic practices themselves. That forces platform and pricing-tool redesigns, changes consent flows in e-commerce, and raises enforcement questions for regulators and legal teams.
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What This Bill Actually Does
The amendment adds a set of new, regulation-ready definitions—online retailer, online distributor, online platform, electronic shelf labelling system and personalized algorithmic pricing—so the Act can target digitally mediated sales. By building the definitions with a 'subject to the regulations' caveat, the legislature gives ministers the power to narrow or clarify who and what systems fall inside the law later, which means immediate obligations depend partly on forthcoming regulations.
Substantively, the bill inserts explicit prohibitions into the list of unfair business practices. If a price is shown via an electronic shelf labelling system, the supplier cannot demand a higher price at point of sale because an algorithm personalized that price for that consumer.
Separately, online retailers and online distributors cannot use personalized algorithmic pricing to increase the price demanded of a consumer on their platforms. Those two strands capture in-store digital price displays and platform-based e-commerce.To strengthen consumer choice, the bill declares that the use of personalized algorithmic pricing is a material fact—meaning consumers should be able to rely on disclosure norms—and sets a high bar for when a consumer is treated as having given express consent to a higher algorithm-generated price: the supplier must both make a prominent, clear disclosure of the reason for the higher price and the consumer must take clear, overt action to accept it.
The bill therefore pushes sellers away from passive or implied consent models used in many low-friction checkout flows.The Act also expands how tribunals and courts evaluate unfair practices: it lists contextual factors (including disclosure of mandatory fees, price comparability and data access) and makes clear an unfair practice can occur through algorithms, data analytics, machine learning or AI even if no transaction is completed. Finally, the bill shortens the prosecution limitation to two years measured from when the director first knew evidence sufficient for charges, and leaves the statute to come into force on assent.
The Five Things You Need to Know
The bill defines 'personalized algorithmic pricing' to include prices set or varied by algorithms using data points such as browsing history, device profiles, inferred willingness to pay, income, credit history, location and health information (with regulators able to add more).
It adds two explicit prohibitions: charging a consumer a higher price at point of sale where an electronic shelf labelling system is used, and online retailers/distributors using personalized algorithmic pricing to increase the amount demanded from a consumer.
The bill deems the use of personalized algorithmic pricing a 'material fact' and requires express consent for online price surcharges—meaning prominent disclosure plus clear overt consumer action is needed to lawfully impose a higher algorithmic price.
A new clause makes algorithmic activity an unfair business practice even if no sale occurs, letting regulators pursue deceptive or abusive pricing systems themselves rather than only completed transactions.
The statutory limitation for prosecution is replaced: prosecutions must start within two years after the day evidence sufficient to justify a prosecution came to the director's knowledge, certified by the director.
Section-by-Section Breakdown
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Creates digital-era definitions and delegates details to regulation
The Act inserts definitions for consumer transaction, electronic shelf labelling system, online distributor, online platform, online retailer and personalized algorithmic pricing. Each of the digital terms is expressly made 'subject to the regulations,' giving the government room to specify thresholds (for example, user counts, revenue tests, or technical standards) and to narrow the scope. For compliance teams, that means the practical reach of the law will depend heavily on follow-up regulations and guidance from government.
Adds precise prohibitions on algorithm-driven price increases
The bill amends the enumerated unfair practices to prohibit demanding a higher price at point of sale when the price was displayed by an electronic shelf labelling system, and to prohibit online retailers/distributors from using personalized algorithmic pricing to raise prices demanded from consumers. This ties the prohibition to two delivery contexts—physical stores using ESLs and online marketplaces—so enforcement can target both in-person and platform-based behavior.
Makes algorithmic pricing a material fact and raises the consent bar
Use of personalized algorithmic pricing is declared a material fact for disclosure analysis, which shifts the legal baseline for what must be revealed to consumers. The bill also defines when express consent exists for online suppliers: a prominent, clear disclosure explaining the higher price plus a consumer's clear overt action to accept it. This removes implied or passive acceptance as sufficient in algorithmic surcharge scenarios and will force checkout and UI changes on platforms.
Specifically deems certain algorithmic price differentials to be 'taking advantage'
Section 8.1 lists scenarios that constitute taking advantage—e.g., exploiting a consumer's infirmity or incapacity—and adds a clause that an online retailer/distributor or any supplier using an electronic shelf labelling system takes advantage if it demands a higher price than that offered to other consumers because of personalized algorithmic pricing. This provision targets discriminatory price hikes and anchors remedies to longstanding doctrines of unconscionability.
Gives decision-makers factors to weigh and allows action absent a sale
The bill adds factors for determining unfair practices, such as price comparability (except where personalized algorithmic pricing is used), the clarity and prominence of disclosed fees for online sellers, whether there is reasonable probability of full payment, and whether data available to suppliers is also available to consumers. It further states that an unfair business practice may occur through algorithms or AI even if no transaction is completed, opening enforcement against systems that may mislead, manipulate or prepare consumers for exploitative pricing.
Shortens prosecution clock to two years from director's knowledge
The previous limitation scheme is replaced with a rule that prosecutions must commence within two years after evidence sufficient to justify prosecution came to the director's knowledge, with the director's certificate serving as proof of that date. That change accelerates enforcement timelines and centralizes the timing determination in the director's office.
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Explore this topic in Codify Search →Who Benefits and Who Bears the Cost
Every bill creates winners and losers. Here's who stands to gain and who bears the cost.
Who Benefits
- Consumers—especially those vulnerable to targeted price discrimination—gain clearer rights against individualized algorithmic surcharges and stronger grounds to challenge unfair online pricing.
- Consumer protection regulators and enforcement agencies receive explicit statutory tools to pursue algorithmic pricing practices themselves, not just finished transactions, making it easier to investigate pricing systems and stop harmful systems early.
- Small brick-and-mortar retailers and non-personalized sellers benefit competitively if targeted online price hikes that undercut fair pricing are curtailed.
Who Bears the Cost
- Online retailers and marketplace operators must redesign pricing flows, disclosure language and consent mechanisms and may need to disable or reconfigure personalization features to avoid liability.
- Pricing-engine vendors, data analytics firms and algorithmic service providers will face compliance costs to document models, supply audit trails, and alter product configurations for Saskatchewan customers.
- Suppliers using electronic shelf labelling systems must reconcile remote updates with point-of-sale pricing and invest in system checks or rework ESL workflows to avoid inadvertent higher charges.
Key Issues
The Core Tension
The central dilemma is balancing protection against discriminatory, exploitative individualized pricing with preserving lawful, efficiency-enhancing uses of algorithms; protecting consumers pushes toward bright-line bans and strict consent rules, while preserving adaptive pricing, dynamic inventory management and seamless e-commerce demands flexibility that the bill constrains.
The bill leaves critical definitional detail to future regulations, which creates short-term uncertainty about who exactly will be covered and which technical systems count as ESLs, online platforms or distributors. That delegatory approach gives regulators flexibility but forces businesses to prepare for a moving target: compliance programs may need to be modular to adapt once regulations finalize.
Enforcement will hinge on proving the link between an algorithm's outputs and the higher price demanded; regulators will need technical expertise and access to model inputs, training data and decision logs to make cases stick.
Operational tensions are real. The express-consent requirement for online price surcharges undermines the low-friction checkout flows that many e-commerce businesses rely on: inserting a prominent disclosure and obtaining clear, overt consumer action can increase abandonment rates and complicate cross-border sales when only Saskatchewan consumers are covered.
The clause allowing action even when no transaction is completed is a double-edged sword: it enables early intervention against abusive systems but raises questions about what preparatory behaviors or tests amount to an unfair practice and risks chilling legitimate algorithmic experimentation and A/B testing.
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