Can Algorithmic Pricing Disclosures Build Consumer Trust?

Starting this summer, New Yorkers shopping online or booking rides might spot a new warning tucked beside the price: “This price was set by an algorithm using your personal data.”

The requirement, effective July 8 under New York’s sweeping new law, marks the first time a U.S. state has mandated such algorithmic pricing disclosures for businesses that personalize prices based on consumer information. But as litigation brews and regulators across states and continents take note, the implications are destined to reverberate well beyond the Empire State. It also raises a broader question that every business using dynamic pricing must now confront: will transparency build trust, or will it drive customers away?

From Lawsuits to Labels

Algorithmic pricing has already been challenged in the courts. Regulatory bodies have ramped up scrutiny of algorithmic pricing in antitrust and discrimination contexts. The Department of Justice has alleged that RealPage’s rental pricing software allowed landlords to coordinate rents, according to a May CPI report. Hotels are facing similar claims in a class action over revenue management tools.

Legal commentary has warned that algorithms using personal or competitively sensitive data can raise antitrust and create complex risk around fairness, privacy and discrimination concerns. But disclosure rules like New York’s shift the focus. Instead of asking only whether algorithms are legal, companies must now ask how consumers react when told directly that their data helped set the price.

The Trust Gap in Dynamic Pricing:

Transparency doesn’t always translate to trust. As PYMNTS reported, many consumers view personalized pricing as inherently unfair, even when it reflects real-time supply and demand.

Ride-hailing apps have been criticized for surge pricing during emergencies. Airline passengers bristle when ticket prices fluctuate wildly day to day. eCommerce shoppers often suspect manipulation when prices change after cart abandonment. If disclosures make these practices explicit, the risk is that consumers see them less as efficiency and more as exploitation. Evidence from tech and retail points to a trust gap: shoppers worry that algorithms might penalize them for data they can’t control or don’t fully understand.

Delta Air Lines’ case adds urgency to the disclosure debate. The airline pushed back on accusations that it uses artificial intelligence (AI) to set individualized fares. Delta said it relies on aggregated data to analyze routes, demand and competition, not personal data tied to individual customers, in response to concerns raised by lawmakers about “personalized pricing.”

Still, critics caution that even forecast- and demand-driven pricing can feel personal if customers believe their individual behavior or identity might have influenced what they are paying.

Governments at all levels are paying attention. Seattle has already considered banning rent-setting algorithms outright. Canada has debated whether cartel-style rules should apply to algorithmic pricing tools. Europe’s AI Act could set yet another standard. Businesses everywhere will soon face some form of accountability for how algorithms influence the prices consumers pay.

From Compliance Burden to Competitive Strategy

While many firms will treat disclosure as a compliance box to check, others may see an opportunity.

The National Retail Federation, in challenging New York’s law, has argued that pricing algorithms help its members deliver lower costs to shoppers, essentially making the case that “our algorithms save you money in real time.”

Others could head in the opposite direction, branding themselves as “algorithm-free” or “fair-priced” to attract customers who value predictability and transparency over constant fluctuation.

Together, these two approaches suggest that algorithmic disclosure could spark a new form of competition, not just on price, but on how pricing itself is framed to consumers. As PYMNTS noted, the efficiency gains of pricing software are clear. But in a market where transparency is mandatory, the differentiator may be how companies present the use of algorithms, either as a feature or a flaw.

The Bigger Picture

Algorithmic pricing is no longer just a legal or technical issue. It is becoming a matter of consumer psychology and brand positioning. New York may be the first state to require explicit disclosure, but the underlying tension among efficiency, fairness and trust is global.

The question businesses must answer is not simply whether their use of pricing algorithms complies with the law. It is whether customers, once told their data drives the price, will keep buying and whether some competitors will seize the moment to turn transparency itself into a selling point.