AI & Law: Antitrust Vigilance And AI

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Using AI for antitrust vigilance

by Dr. Lance B. Eliot

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Key briefing points about this article:

  • Antitrust law has once again been brought to the forefront of society
  • A recent spate of antitrust lawsuits against tech firms catches the eye and worldwide attention
  • We need to be cautious in overstepping our instant assumption that antitrust has occurred
  • To aid in the antitrust vigilance proposition we can adopt the use of AI for antitrust endeavors
  • Via the use of AI-based antitrust diligence systems, the vigilance can be notably enhanced


It seems that the daily news is filled with breathtaking stories about antitrust violations, or at least alleged such violations.

Of course, we have to remain diligent and await the outcome of these antitrust lawsuits before we can summarily slam the targeted firms. Antitrust is a complex case to be made and there are abundant chances that the allegations will end-up falling apart.

Give the accused their day in court, one might implore. Yet, despite that sage advice, there is a strong temptation to look at the accused firms and believe that one readily spies what appears to be clear-and-present indications of antitrust practices. Anyway, allow the gears of justice to work their grinding mechanizations and then we will presumably know.

One of the latest headlines entails an antitrust lawsuit filed by nearly forty states that have accused Google of exercising monopolistic power over the online search marketplace. The suit alleges that this vaunted Alphabet Incorporated entity carried out a scheme involving anticompetitive practices, doing so in the outright and flagrant conduct of its business activities and in the contracts that it has insidiously put in place. Meanwhile, Google denies that any such antitrust violations have occurred, explaining that their business efforts and allure to consumers showcase that their online search predominance is highly competitive and proffers the best in product design and technological capability.

The one solace for Google is that they are not the only recent headline-making antitrust-accused tech firm since there have been similar newsworthy antitrust qualms leveled at Facebook and others. Of course, that’s an antitrust-alleged sacred group or special club that few would wish to be a member of. About the only thing we can say for sure is that these antitrust allegations and investigations are likely to play out over a lengthy period, and there are bound to be splintering offshoots that will surface along this quite rocky and painstakingly arduous antitrust-assessing journey.

For anyone interested in particularly excellent research on antitrust in today’s tech spurred world, I highly recommend the work by esteemed colleague Professor Thibault Schrepel at the Utrecht University School of Law (he also serves at the University of Paris, and as a faculty associate at Harvard, plus a CodeX Fellow at Stanford University).

In one of his papers published this year in the NYU Journal of Law & Liberty, he propounds that antitrust laws must not become romanticized and used to pit the tech elites versus the public at large: “Increasing romanticization could critically jeopardize decades of jurisprudential construction, causing economic disruption, destabilization of the law, and blindness towards real anti-competitive practices on the part of antitrust authorities, consequently placing the rule of law at risk” (see link here).

Schrepel mentions this growing concern as a general cautionary wake-up call and not as a direct commentary on these latest headline-grabbing instances. Whether this newest spate of antitrust allegations is a byproduct of the current political climate and polarization, or whether they are straight ahead and unequivocally undeniable instances of antitrust is a matter yet to be ascertained.

You might also relish his paper entitled “Predatory Innovation: The Definite Need for Legal Recognition” that appeared in the SMU Science and Technology Law Review in 2018. In this cornerstone piece, he defines and proffers that we need to encapsulate predatory innovation into the vernacular of antitrust law: “In fact, the terms of predatory innovation — which the author defines as the alteration of one or more technical elements of a product to limit or eliminate competition — describes all practices that, under the guise of real innovations, are anti-competitive strategies aimed at eliminating competition without benefiting consumers” (see link here).

This emphasis is especially poignant and strikes prophetically at the heart of the current suit against Google since a crucial element that underpins the allegations involves Google having implemented new features into its search engine that can be judged as either cast for purely competitive reasons (their presumed assertion) or undertaken by employing a monopolistic practice to freeze out their competitors.

AI As An Aid To Antitrust Vigilance

Shifting gears, the topic of antitrust also raises another interesting facet about the future of the law, namely the gradual adoption of Artificial Intelligence (AI) into the law and the leveraging of autonomous legal reasoning systems. For details about the latest pace and trends of AI and legal reasoning, see my book entitled “AI and Legal Reasoning Essentials” at this link here:

How will AI be instrumental and integrated into antitrust modernizations?

I’ve indicated that the use of AI will be infused into the entire lifecycle of antitrust undertakings (see my research article at this link here).

For each of the stages or phases of an antitrust effort, you can expect that AI will be imbued into the activities and will at times aid the lawyers and human-conducted efforts and will at other times be working autonomously to grind through antitrust pursuits. Based on the U.S. Department of Justice (DOJ) Antitrust Division Manual, there is ample opportunity for AI and computer-based smart systems to substantively enhance today’s principally manually laden and paper-pushing antitrust investigative procedures and case development processes.

My framework defines the antitrust lifecycle as consisting of six major stages or phases, and for which AI will become a vital collaborator in all crucial respects:

1) Detection — seeking to identify potential antitrust violations

2) Assessment — ascertaining if there is a civil or criminal case of prosecutorial merit

3) Investigation — establishing (or not) the case to support an asserted antitrust violation

4) Recommendation — indicating whether a formal civil or criminal suit should be launched

5) Prosecuting — aiding or carrying out the antitrust case in our courts

6) Implementation — undertaking the required judgment monitoring and enforcement

Briefly, consider for example the first stage that entails the detection of a potential antitrust violation.

This first-step aspect of detection or discovery is a lot harder to achieve than a cursory glance might so suggest. The AI would use Natural Language Processing (NLP) capabilities to analyze complaints lodged by consumers and businesses that claim antitrust conduct is taking place in the marketplace. Customized NLP would also be continually assessing media reports about antitrust potentialities. Furthermore, information from government informants that have applied for leniency under the antitrust leniency programs would be examined, and so would the formal complaints filed by U.S. Attorneys and by the states. Etc.

This is a voluminous amount of data that by-hand is overwhelming to keep up with. Sophisticated AI techniques combined with the NLP, such as the latest in Machine Learning and Deep Learning, would be able to computationally continually be scanning and assessing such material. In a likewise fashion, each of the other stages of the antitrust process can be advanced via the use of AI. AI for antitrust provides scalability and would inure a greater use of data-based metrics and transparency in antitrust vigilance.


Though you might assume that the AI would also resolve and prevent the occurrence of politically motivated antitrust accusations, do not be so sure about that presumptive thought. Those that adapt and shape the AI can still force their imprint upon what the AI will be doing.

As a final comment, the Federal Trade Commission (FTC) refers to the DOJ Antitrust Division as The Enforcers of the antitrust laws. AI can become a companion enforcer, albeit the word “enforcer” in this context certainly seems ominous and reminiscent of those sci-fi world-dominance scenarios. Maybe we should refer to the AI for antitrust vigilance as The Protector or perhaps The Guardian (well, those could be equally disquieting, one supposes).

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Copyright © 2020 Dr. Lance Eliot. All Rights Reserved.

Dr. Lance B. Eliot is a renowned global expert on AI, Stanford Fellow at Stanford University, was a professor at USC, headed an AI Lab, top exec at a major VC.

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