Surge Pricing in the Age of AI: Fairness or Exploitation?

Dynamic pricing has long been present in markets. As seats fill up, airlines adjust their prices upwards, ride-hailing apps increase fares when it's raining heavily, and hotel prices change depending on the season. What is new in the current economy is the use of advanced Artificial Intelligence (AI) to optimize pricing in a personalized way, at scale. Rather than simply responding to shifts in supply and demand in broad strokes, algorithms can now (at least in theory) ascertain the maximum price an individual will pay and charge them that price accordingly.

This is a pressing question. Is AI-powered surge pricing merely an efficient method of allocating resources, or is it a line-crossing digital-age form of exploitation?

From Supply and Demand to Individual Pricing

The notion of surge pricing is rooted in scarcity. When demand for taxis increases during a storm, rising prices encourage more drivers to combat greater relative demand in a real-time market for taxis. In this case, the price reflects the value of that taxi ride. When the supply of taxis is higher, the price will shift in the favour of the consumer until the market reaches an equilibrium.

But artificial intelligence can be used to do something much more granular, such as first-degree price discrimination. Based on analysis of browsing history, spending behavior, and even device types, the AI can better predict one's willingness to pay.

For instance:

1. A frequent traveler could pay more for the exact flight as a vacationer.
2. Two users searching for headphones on the same website could receive different prices based on their respective purchasing behaviors.

While economists have theorized about the possibility of "perfect" price discrimination, AI now puts it within reach of consumers and the economy.

Efficiency vs. Fairness

From an efficiency perspective, personalized pricing can help firms maximize revenues and minimize waste. Firms extract more surplus from consumers, and in some cases, a consumer may be able to purchase a product at a lower price who would otherwise be priced out.

However, fairness issues take center stage in this discussion:

• Transparency - consumers may not understand why they are paying a higher price compared to others.
• Inequality - wealthier individuals may consistently pay higher prices, leading to entrenched social inequalities.
• Trust - if a consumer feels that a personalized price is unfair, the consumer will likely retaliate and stop using the service or good altogether (this was demonstrated in a negative consumer reaction to ride-hailing tech companies when prices spiked during a natural disaster).

Initially efficient price allocation devolves into a situation of deteriorating social trust in markets.

Learning from Real-World Backlash

History provides warnings.

• In 2014, an upside for Uber quickly became a backlash when fares were seen as exorbitant during a hostage crisis in Sydney, leading Uber to alter its surge fare structure.
• Amazon proposed personalized DVD pricing in the early 2000s but quickly abandoned the practice in the face of consumer knowledge.

Each of these moments brings forth a similar theme: although the markets will accept generalized surge pricing, individualized, opaque gouging/pricing is not tolerated.

The Importance of Regulation and Consumer Protection

-As AI technology expands, regulation will become increasingly necessary to balance innovation with equity and fairness. Regulation may serve a variety of purposes, including:

-Transparency mandates- or laws that require firms to disclose when prices are being dynamically personalized

-Limits on surge pricing, especially in basic necessities of life like medical care or transportation

-Limits on use of data- for example, limiting the extent to which price algorithms can utilize the analysis of consumer behavior

The goal of these and other forms of regulation would not be to entirely ban dynamic pricing, but instead to prevent it from becoming predatory.

Establishing Trust in the AI Economy

Organizations utilizing AI-enhanced pricing should bear in mind the long-term impacts to consumer trust. A profit-maximization approach is likely to backfire if consumers feel that they are being manipulated.

Some fintech firms appear to have a different approach. For example, organizations are seeking accounts that are digital-first and globally scalable. Altery, with its business account characterized as being transparent and ready for API access, demonstrates how financial services can cultivate efficiency and fairness, which is worth emulating in AI pricing approaches.

Evaluating Market Boundaries

The newest frontier in economics is AI-based surge pricing, where value is determined at the margins. If an algorithm can allocate resources precisely, it, along with market forces, has the power to disappoint or devastate our trust at scale.

This returns to the question - will society accept that firms can charge us the maximum that we can pay? Markets are based on a bit more than mathematical efficiency. They rely on trust, fairness, and some shared norms of behavior. In a market without trust, fairness and norms, no matter how smart, an algorithm risks rupturing the invisible contract that exists between firms and consumers.