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Artificial intelligence (AI) is transforming how companies set prices – but experts warn algorithm-driven personalised pricing risks alienating consumers and inviting regulatory backlash.

Picture this: you’re searching Google Flights for a round trip from Sydney to New York. You spot a decent price on one website, and then, just out of curiosity, you click on a business class fare to compare the prices. When you return later with your credit card ready, the original ticket prices have soared. You try again using a VPN, only to see even higher prices this time. 

Welcome to the age of AI-driven pricing – an integral part of the airline industry’s (and others') expanding revenue management and price discrimination ecosystem, where algorithms may flag you as a business traveller likely to pay more for a last-minute seat.

The move toward AI-driven personalised pricing has raised some fundamental issues of fairness and consumer trust. Specifically, some  that dynamic airline pricing relies heavily on personal data, which some say  from smart targeting to outright unfair practice. With companies like OpenAI enabling ever more sophisticated AI systems, some economists and lawmakers say consumers need more explicit protections.

���������¼� Business School’s , an expert in consumer behaviour, cautions that while AI pricing can maximise profits in the short term, it can erode trust. While airlines and retailers may see AI pricing as a way to set prices more efficiently, Prof. Garg says the strategy, if misused, will be “very short-sighted” and “very risky.”

“You might make a profit now, but you will lose your customers in the long-term,” she says, pointing to  showing consumers reject deals they feel are unfair, even if those deals save them money.

The consumer backlash risk

Different industries have used – adjusting prices in real time based on demand, supply, customer behaviour, or market conditions – for decades. On , for example, it can seem like prices change every 10 minutes. While this is a common practice that has been studied in the past, what’s new is the rise (and impact) of  on dynamic pricing models, which industries can now use to personalise individual prices faster and more efficiently.

However, Prof. Garg warns this growing trend raises issues of fairness and trust. “From a profit perspective, dynamic pricing makes sense. You're tapping into how much the marginal value of something is to a particular consumer, and you're maximising your revenue based on that,” she says. 

“Traditionally, marketers have done this with a time lag – think of strategies like ‘’. But now, it’s instant, and based on your personalised data.”

That data can include sensitive signals such as zip code, income level, or credit history. “You could literally say, ‘Oh, you live in X zip code, so I will charge you more, or I'll show you a higher price’,” she says. 

“It’s not fair, and I am not sure how this is getting past the regulators. Consumers will typically put up with a lot till they figure out that, you know, there is blatant unfairness, because that goes against the basic moral and social norms we have.”

Dynamic pricing and airline tickets

The airline industry that adjust flight prices based on demand and timing. Today, major carriers are starting to use AI models to refine this further. For example, trialled personalised plane ticket pricing powered by AI last year, sparking debate about transparency and fairness.

But Prof. Garg says the practice could even  “The worrisome aspect is that this could impact almost any product. How do I know that the price I see for bread or bananas on Coles’ website is the same price that another consumer sees? I have no way of knowing,” she says.

While she says consumers are better at spotting anomalies associated with surging like bananas, they are not as good at spotting anomalies with infrequent purchases such as airfares, accommodation, or car hire – making those markets ripe for misuse.

“The implications transcend sectors and industries. The more opaque the buying process, and the less frequently we purchase something, the bigger the issue becomes, because we don’t have a good reference point for prices for those purchases,” she says.

The economic rationale for personalised pricing

���������¼� Business School economist explains that dynamic pricing is not new from an economics perspective. “Firms typically want to get the maximum price they can for what they are selling. If they can charge consumers a higher price on a certain day or at a particular time, then they will,” he says.

It also isn’t inherently harmful if it delivers convenience to the consumer. “Examples of this kind of pricing behaviour include surge pricing by ride-sharing companies, holiday-period airfares, or umbrella prices depending on the weather,” he says.

Some people are simply happy to pay more to have access to the products or services when they need them. “If firms are also able to distinguish between types of consumers and offer prices to different consumers at any time (i.e. engage in price discrimination), then they can make extra revenue by reducing consumer surplus – the benefit that consumers get by purchasing products at a price lower than they are willing to pay.”

Ride-sharing surge pricing is one example where consumers benefit: off-peak rides can be cheaper, while higher peak prices ensure supply. “Personalised pricing can have significant benefits to many consumers. The popularity of ride-sharing services reflects this: prices can be lower than for taxis during off-peak periods, while surge pricing ensures that there is enough supply at peak times for those who can afford it.”

But, he adds, there are legitimate equity issues: “Consumers who are unable to pay the peak period price will have to wait for a taxi, typically experiencing a long wait, with all the costs associated with this.

“Stores may realise that poorer consumers will pay higher prices than richer consumers, as they can’t go to another store due to time and transport costs. The same applies to older people and those with disabilities who are unable to change their shopping routines easily.”

Throw AI into the mix, and these issues are amplified. “By using large amounts of consumer data, real-time updating of market conditions and continuous learning, AI can make it quicker and easier for firms to adjust prices to extract the highest price possible from each consumer,” Prof. Fox says.

Regulators playing catch-up on AI

Price discrimination is so long as it doesn’t discriminate based on race, gender, disability, or age or stifle competition. However, Prof. Fox explains that regulators are increasingly concerned about transparency and algorithmic misuse.

The ACCC, for example, has made algorithmic transparency a . And while Australia has no AI-specific laws yet, its consumer protection framework – notably the under the Competition and Consumer Act 2010 – is being reviewed to address unfair practices like dynamic pricing.

While Prof. Fox notes that the Australian Bureau of Statistics already tracks across sectors and is well-placed to monitor pricing trends, he says “regulatory authorities will likely need to be better resourced to effectively monitor for illegal price discrimination.”

Prof. Garg agrees. “Regulation always lags behind innovations. Firms are already tempted to, in my opinion, misuse the technology before rules and regulations can come in. 

“As a consumer, for example, I give you access to my personal data because you won’t let me buy something otherwise. But there’s an implicit agreement that you won’t abuse that information," she says.

“I feel that there are more avenues for redress, so exploitative practices will work less in the Australian market versus the American market," adds Prof. Garg.

The experts agree that swift regulatory action is needed. While AI-driven pricing can boost efficiency and benefit consumers, it risks inequality, erosion of trust, and backlash if left unchecked.

“Anytime marketers or firms try to abuse consumer trust, it backfires eventually,” adds Prof. Garg.