Algorithms, Artificial Intelligence And Joint Conduct

INTRODUCTION

The ability of algorithms and artificial intelligence to monitor and set prices is increasing in sophistication, effectiveness and independence from human involvement at an exponential rate. The growth in this area, which is seen simultaneously across a range of AI applications, is such that no one — even its creators — is likely to fully appreciate AI's capabilities until sometime after they have been realized. Pricing "bots" are already capable of engaging in behavior that we would not hesitate to call "parallel conduct" if it were performed by humans, and they will only get better at it. Indeed, the day may not be so far off when the pricing bot of one firm is fully capable of colluding — in every meaningful sense — with the pricing bot of a competing firm. At that point, we may have "conspiracy" cases under Section 1 of the Sherman Act that look very much like the cases we have today, except that the parts now played by humans are played by robots.2

The few existing antitrust cases involving pricing algorithms have not crossed this Rubicon, or really even approached it. They do not involve joint conduct by bots, in any sense. Instead, these cases involve human beings reaching familiar price-fixing agreements and then implementing them algorithmically. While these cases may create special problems of detection and proof, at least for the moment they do not seem to require any shift in the conceptual apparatus we use to solve antitrust problems.

There is reason to think such a shift may be coming, however. Joint conduct by robots is likely to be different — harder to de- tect, more effective, more stable and persistent — than traditional joint conduct by humans. For example, one of the basic precepts of the Sherman Act is that "unilateral" conduct by firms in the same market is not unlawful under Section 1, even if the conduct is closely interdependent and predictably yields supracompetitive prices that would be per se unlawful if achieved by agreement. An unspoken premise of this time-honored rule is that such interdependent conduct is likely to be relatively unstable in the absence of an agreement, and therefore, with any luck, the supracompetitive effects generally will be shorter lived and less pernicious than if they were achieved through true joint conduct.

But this premise may have less force in a world of bots, who can interpret and respond to the actions of their competitors with far more precision, agility and consistency than their human counterparts. By simply allowing these bots to go to work, it is easy to imagine an effectively permanent pricing stasis settling over many markets, and not always with procompetitive effects.

How will enforcers approach such conduct, much less disrupt or prevent it? What duties should we impose on human beings to ensure their bots behave, and what culpability should they have when their bots go astray? The next ten years will begin to provide the answers, but the technology is already well ahead of the law, and the growing pains are likely to be immense.

BACKGROUND

A few months before the Sherman Act passed Congress on July 2, 1890, the U.S. Census Bureau started using Herman Hollerith's elec- trochemical punched card tabulator machines to record census returns. This invention allowed the Census Bureau to collect much larger volumes of data and reduced the amount of time to process census results. Hollerith's invention laid the groundwork for automated data processing, and he later partnered up with other inventors to form the technology company that ultimately became IBM.

Over time, engineers, inventors and entrepreneurs developed more advanced versions of Hollerith's data machines and imple- mented them in the marketplace. In the early 1970s, Thomas Peterffy and Dr. Henry Jarecki pioneered the use of computer algorithms that weighed various factors relating to option pricing. Their "black boxes" would "inhale market data, chew on it, then issue an instruction to their user, in this case whether to buy or sell."3 Their use of algorithmic pricing gave them an edge in the commodities markets because their computers would be able to process data inputs, weigh each factor and make trading recommendations more adeptly than their human counterparts.4

Today, modern innovations include more advanced algorithms, adaptive technologies and artificial intelligences (e.g. IBM's Watson, Microsoft's Oxford, Google's DeepMind and Baidu's Minwa). These technologies can pore over vast amounts of data before recommend- ing or making strategic decisions.5 Like the simpler machines of the past, the newer machines can use data processing and analytics to give companies an edge in the marketplace when it comes to production, pricing and other business operations.

While the application of technology to determine purchasing and pricing patterns is nothing new, the increased sophistication of such technologies and their potential to play a role in unlawful conduct has caught the attention of global antitrust and competition enforcers. In a speech given on March 16, 2017, Commissioner Margrethe Vestager discussed how the use of algorithms could infringe EU competition law.6 She commented that "[p]ricing algorithms need to be built in a way that doesn't allow them to collude" and that "companies can't escape responsibility for collusion by hiding behind a computer program."7

Perhaps more notably, the U.S. Department of Justice ("DoJ") has already indicted two individuals for their use of the same pricing algorithms in the online poster marketplace.8 At the time of these indictments, many commentators noted that these cases could start a new trend for price-fixing cases. But once the dust settled, it became apparent that DoJ's cases did not reveal a new species of a Section 1 conspiracy. After all, the online poster cases still appeared to rely upon direct evidence of an agreement to establish the underlying antitrust violation.

While these rapidly developing technologies have not yet changed any substantive antitrust law, the future of Section 1 cases involving sophisticated pricing algorithms and artificial intelligence ("AI") may pose some new legal...