' Jordan Wampler | MTTLR

Act 2 Enforcement for Antitrust and Algorithms

If algorithms can learn and develop means of achieving business efficiencies beyond the initial parameters set by their programmers, regulators will need to employ creative means of enforcing antitrust violations. The lack of clarity on whether section 1 agreements under the Sherman Antitrust Act have been made—and on what parties were involved in making said agreements—cuts in favor of finding liability under section 2. Pricing algorithms have been used generally to procompetitive ends. With the ability to efficiently process huge quantities of data and respond to consumer almost immediately, more and more businesses are adopting pricing algorithms. The uptick in algorithm adoption and, consequently, data accessibility has created price transparency—this benefits consumers, who generally like to price compare before purchasing. In a world where firms are adopting proprietary price algorithms, anticompetitive effects are not immediately apparent. Of course, things aren’t that simple. The development of artificial intelligence has created a world where algorithms are not solely the product of human creation. Where algorithms were previously limited by the parameters outlined programmers, artificial intelligence and neural networks have opened the door for algorithms to dynamically derive targeted outcomes. This integration of artificial intelligence, neural networks, and traditional algorithmic coding raises risk of potential antitrust liability for well-meaning firms. Pricing algorithms may possibly integrate with competing algorithms and result in price fixing absent human agreement. The development of these types of intelligent algorithms has firms concerned about their exposure to antitrust liability in situations where they are passive participants in cartels formed by their intelligent pricing algorithms. The European Commissioner for Competition, Margethe Vestager, has noted that the onus is on...