Month: August 2023

Lemley on How Generative AI Turns Copyright Law on Its Head

Mark A. Lemley (Stanford Law School) has posted “How Generative Ai Turns Copyright Law on its Head” on SSRN. Here is the abstract:

While courts are litigating many copyright issues involving generative AI, from who owns AI-generated works to the fair use of training to infringement by AI outputs, the most fundamental changes generative AI will bring to copyright law don’t fit in any of those categories. The new model of creativity generative AI brings puts considerable strain on copyright’s two most fundamental legal doctrines: the idea-expression dichotomy and the substantial similarity test for infringement. Increasingly creativity will be lodged in asking the right questions, not in creating the answers. Asking questions may sometimes be creative, but the AI does the bulk of the work that copyright traditionally exists to reward, and that work will not be protected. That inverts what copyright law now prizes. And because asking the questions will be the basis for copyrightability, similarity of expression in the answers will no longer be of much use in proving the fact of copying of the questions. That means we may need to throw out our test for infringement, or at least apply it in fundamentally different ways.

Gal & Rubinfeld on Algorithms, AI, and Mergers

Michal Gal (U Haifa Law) and Daniel L. Rubinfeld (U Cal Berkeley Law; NBER; NYU Law School) have posted “Algorithms, AI and Mergers” (Antitrust Law Journal (2023)) on SSRN. Here is the abstract:

Algorithms, especially those based on artificial intelligence, play an increasingly important role in our economy. They are used by market participants to make pricing, output, quality, and inventory decisions; to predict market entry, expansion, and exit; and to predict regulatory moves. In a growing number of jurisdictions, algorithms are also used by regulators to detect and analyze anti-competitive conduct. This game-changing switch to (semi-)automated decision-making has the potential to reshape market dynamics. While the effect of algorithms on coordination between competitors has been a focus of attention, and scholarly work on their effects on unilateral conduct is beginning to accumulate, merger control issues have been undertreated. Accordingly, this article focuses on such issues.

The article identifies six main functions of algorithms that may affect market dynamics: collection and ordering of data; improving the ability to use existing data; reducing the need for data, for in-stance by generating synthetic data; monitoring; predicting, to deter-mine how different types of conduct, including mergers, are likely to affect market conditions; and decision-making.

The article demonstrates how such algorithms can exacerbate anti-competitive conduct with respect to both unilateral and coordinated effects. Towards this end, seven scenarios are explored: collusion, oligopolistic coordination, high unilateral prices, price discrimination, predation, selective pricing (in which a buyer offers a higher price to some suppliers in an aggressive bid for an input), and reducing the interoperability of datasets. For each scenario, we analyze how the market conditions necessary for such conduct are affected by algorithms.

These findings are then translated into merger policy. Algorithms are shown to affect substantive as well as institutional features of merger control. Algorithms also challenge some of the assumptions that are ingrained in merger control, suggesting that a more informed approach to some algorithmic-related mergers is appropriate.