Should Computers Decide How Much Things Cost?

Article by Colin Horgan: “In the summer of 2012, the Wall Street Journal reported that the travel booking website Orbitz had, in some cases, been suggesting to Apple users hotel rooms that cost more per night than those it was showing to Windows users. The company found that people who used Mac computers spent as much as 30 percent more a night on hotels. It was one of the first high-profile instances where the predictive capabilities of algorithms were shown to impact consumer-facing prices.

Since then, the pool of data available to corporations about each of us (the information we’ve either volunteered or that can be inferred from our web browsing and buying histories) has expanded significantly, helping companies build ever more precise purchaser profiles. Personalized pricing is now widespread, even if many consumers are only just realizing what it is. Recently, other algorithm-driven pricing models, like Uber’s surge or Ticketmaster’s dynamic pricing for concerts, have surprised users and fans. In the past few months, dynamic pricing—which is based on factors such as quantity—has pushed up prices of some concert tickets even before they hit the resale market, including for artists like Drake and Taylor Swift. And while personalized pricing is slightly different, these examples of computer-driven pricing have spawned headlines and social media posts that reflect a growing frustration with data’s role in how prices are dictated.

The marketplace is said to be a realm of assumed fairness, dictated by the rules of competition, an objective environment where one consumer is the same as any other. But this idea is being undermined by the same opaque and confusing programmatic data profiling that’s slowly encroaching on other parts of our lives—the algorithms. The Canadian government is currently considering new consumer-protection regulations, including what to do to control algorithm-based pricing. While strict market regulation is considered by some to be a political risk, another solution may exist—not at the point of sale but at the point where our data is gathered in the first place.

In theory, pricing algorithms aren’t necessarily bad…(More)”.

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