Tag Archives: Maureen K. Ohlhausen

CCC’s: An Antitrust Lawyer’s Guide to Machine Learning (Guest Post by Ai Deng PhD.)

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Below is a post by valued guest contributor, Ai Deng, PhD. of Bates White Economic Consulting.

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There has been growing interest in the legal community in artificial intelligence (AI), and more specifically in machine learning (ML). This recent interest in AI is at least in part driven by concerns about algorithmic collusion, i.e., the possibility that computer algorithms could ultimately collude on their own, without human facilitation.

There is no question that the antitrust community is largely playing catch-up when it comes to the technical subject matters of AI and ML. As the Acting Chair of the Federal Trade Commission Maureen K. Ohlhausen noted, “The inner workings of these tools are poorly understood by virtually everyone outside the narrow circle of technical experts that directly work in the field.”

While there is no point to antitrust attorneys understanding the nuts and bolts of AI and ML technology, a basic understanding is necessary to better understand and assess the implications of the AI/ML research on antitrust and related legal and economic issues. That is the motivation behind my latest article. Through a series of simple examples, I introduce some fundamental concepts in ML. Along the way, I also discuss a wide variety of ML applications in the law and economics field. I conclude with a brief discussion of the hot topic of algorithmic collusion.

You can download the paper here https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3082514

As always, I appreciate your thoughts and comments. You can reach me at [email protected] or connect with me on LinkedIn [here].

Ai Deng, PhD

Principal

direct: 2022161802 | fax: 2024087838

1300 Eye Street NW, Suite 600, Washington, DC 20005

[email protected]

BATESWHITE.COM

CCC’s: Antitrust and Artificial Intelligence, Empirical Analysis in Class Certification: A Research Update (Guest Post)

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By: Ai Deng, PhD,  Principal, Bates White Economic Consulting

Hope everyone had a wonderful Labor Day weekend. During my time off CartelCapers, I have been working on several research projects. In this post, I’d like to give the interested readers an update on two of them.

When Machines Learn to Collude: Lessons from a Recent Research Study on Artificial Intelligence

From Professors Maurice Stucke and Ariel Ezrachi’s Virtual Competition published a year ago, to speeches by the Federal Trade Commission Commissioner Terrell McSweeny and Acting Chair Maureen K. Ohlhausen, to an entire issue of a recent CPI Antitrust Chronicles, and a conference hosted by Organisation for Economic Co-operation and Development (OECD) in June this year, there has been an active and ongoing discussion in the antitrust community about computer algorithms. In a short commentary (downloadable here), I briefly summarize the current views and concerns in the antitrust and artificial intelligence (AAI) literature pertaining to algorithmic collusion and then discuss the insights and lessons we could learn from a recent AI research study. As I argue in this article, not all assumptions in the current antitrust scholarship on this topic have empirical support at this point.

Sub-regressions, F test, and Class Certification

Did the anticompetitive conduct impact all or nearly all class members? This question is central to a court’s class certification decision. And to answer the question, a methodology—known as sub-regressions (also labelled less informatively as simply the “F test” in the recent Drywall litigation)—is being increasingly employed, particularly by defendants’ expert witnesses. A key step of a sub-regression type analysis is to partition the data into various sub-groups and then to examine data poolability.[1]

Forthcoming in the Journal of Competition Law & Economics, my article titled “To Pool or Not to Pool: A Closer Look at the Use of Sub-Regressions in Antitrust Class Certification” focuses on three areas of interest pertaining to sub-regressions:

  • The related law and economics literature related to this methodology
  • Courts’ recent class certification decisions in cases where parties introduced sub-regression analysis
  • Several methodological challenges, many of which have not been previously acknowledged, as well as potential ways to address them. Specifically, what test should one use? How does one choose the subsets or partitions of data to test? Are individual estimates of damages always the most reliable approach when we believe the impact varies across customers or across some other dimensions?

This paper is currently being processed at the Journal. If you would like a copy, please feel free to reach out to me.

As always, I appreciate your thoughts and comments. You can reach me at [email protected] or connect with me on LinkedIn [here].

Thanks for reading.

Ai Deng, PhD
Principal, Bates White Economic Consulting
Lecturer, Advanced Academic Program, Johns Hopkins University
direct: 2022161802 | fax: 2024087838
1300 Eye Street NW, Suite 600, Washington, DC 20005
[email protected]
BATESWHITE.COM

[1] I first provided an update on this project on CartelCapers here.