The Spring Meeting is the largest gathering of competition, consumer protection, and data privacy profes­sionals globally, with lawyers, academics, economists, enforcers, journalists, and students from around the world. During the Spring Meeting, Axinn associates attended thought leadership panels to capture key insights. Below are the top takeaways from the “What About Bob? Examining Algorithmic Pricing” panel that businesses should keep on their radar.

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Algorithmic pricing tools continue to increase in availability and use, with the potential to help businesses make faster, more informed decisions, increasing productivity. But algorithmic pricing tools have also been under scrutiny by both enforcers and private plaintiffs, such as in the recent RealPage litigation around algorithmic pricing software used to consider pricing in real estate rental markets.

• Algorithmic pricing tools’ unprecedented speed and precision. Price recommendation tools have long been available, but algorithmic pricing tools are a new frontier: software able to incorporate mass amounts of data, “stir it up,” and provide tailored recommendations at a speed and scale beyond the scope of humans’ capabilities.
• Accelerated efficiencies, but also scrutiny.  Panelists acknowledged algorithmic pricing tools’ benefits but observed that enforcers may be concerned where pricing outcomes are the result of fierce competition—for example, being able to instantly update prices in response to new market conditions—but resemble collusion if made by multiple competitors in rapid succession. Additionally, algorithmic pricing tools may incorporate artificial intelligence (“AI”), wherein it is possible the parameters used may be opaque or unknowable.
• Information sharing, or something else? Panelists noted that algorithmic pricing tools do not necessarily allow competitors to share information with each other, though they may incorporate information from competitors, as well as public sources, in their models, especially if multiple firms use the same pricing tools as each other. Regardless, information exchange among competitors can increase competition by allowing them to make faster, more informed decisions about when and how much to increase or decrease prices. But information exchange can also create an appearance of collusion, or facilitate it, depending on what is shared. 
• Public versus private data inputs of particular importance. Courts typically consider many factors to evaluate the legality of information sharing, for example: whether the data collected identifies the sources, or whether it is anonymized; and whether the data is forward-looking, or is stale by days, months, or years. Panelists noted that, based on recent precedent addressing algorithmic pricing, courts may consider particularly important whether the data inputted into and used by algorithmic pricing tools is publicly available, or only privately known.
• Potential for over-enforcement. Panelists expressed concern about the potential over-enforcing of antitrust laws against firms using algorithmic pricing tools. Because algorithmic pricing tools are still nascent, economists generally may not yet understand their competitive impacts. In this early stage of understanding, enforcers may need to be careful of potential false positives in bringing cases involving algorithmic pricing tools to avoid chilling innovation.