The government is launching an inquiry into the use of personal data to set individual prices for holidays, cars and household goods, amid rising fears of a consumer rip-off. The research, supported by the competition watchdog, will explore the prevalence of "dynamic pricing" based on information gathered about an individual, such as location, marital status, birthday or travel history. With about 17% of retail sales now made online, according to the Office for National Statistics, there is rising concern about the use of technology, including artificial intelligence and bots, to "personalise" prices, to the disadvantage of some shoppers. It has become common for online prices to fluctuate depending on time of day or availability – whether for gig tickets or Uber taxis. Now digital labels have begun to appear in shops, offering the potential to bring "surge pricing" into analogue sales.
The Australian Competition and Consumer Commission (ACCC) has instituted proceedings in the Federal Court against trivago NV (Trivago), alleging that the accommodation search engine site misled customers over price. It is alleged by the ACCC that Trivago made misleading pricing representations in its television advertising and on its website from at least December 2013 that breached Australian Consumer Law. The ACCC alleges that the way Trivago's website aggregates deals offered by online travel sites created an impression to customers that it was offering up the best deal. According to the watchdog, Trivago ran TV advertisements that showed its website as an impartial and objective price comparison service that would essentially help customers find the cheapest hotel room prices. But the ACCC argues that in fact, Trivago's website prioritised advertisers who were willing to pay the highest cost per-click fee.
On June 6, 2015, the U.S. Department of Justice brought the first-ever online market-place prosecution against a price-fixing cartel. One of the special features of the case was that prices were set by algorithms. Topkins and his competitors designed and shared dynamic pricing algorithms that were programmed to act in conformity with their agreement to set coordinated prices for posters sold online. They were found to engage in an illegal cartel. Following the case, the Assistant Attorney General stated that "[w]e will not tolerate anticompetitive conduct, [even if] it occurs...over the Internet using complex pricing algorithms."
Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data. Think, for example, of Oracle pursuing an M&A-led strategy for its Oracle Data Cloud data aggregation service, or IBM buying, within the past two years, both The Weather Company and Truven Health Analytics. Early returns for companies making such investments are promising.