This presentation by Michal Gal, Professor of Law, University of Haifa, was made during the discussion “Algorithmic competition” held at the 140th meeting of the OECD Competition Committee on 14 June 2023. More papers and presentations on the topic can be found out at oe.cd/algc. This presentation was uploaded with the author’s consent.

1. Can competition law address
algorithmic harms?
"I never think of the future.
It comes soon enough..."
Albert Einstein
Michal S. Gal
Professor, University of Haifa Faculty of Law
Director, Center for Law and Technology, University of Haifa
President, Academic Society of Competition Law Scholars (ASCOLA)

2. Basic Principles for Regulation
• Algorithms do not prevent application of antitrust
• If a conduct was prohibited if engaged in by a
human, the same applies to algorithms
• There is ALWAYS a human involved at some level
• Algorithms do not change economic principles
– But may change the assumptions at the basis of rules
• Algorithms often create mixed effects

3. AIgorithms as Facilitators of Abuse

4. Algorithmic Predation
Skepticism of feasibility high thresholds for proof
Algorithms challenge such assumptions (Leslie, 2022;
Cheng and Nowag, 2022)
better price discrimination, thereby reducing potential losses
Financing predatory actions by high prices for inframarginal
consumers
Both Low prices Supra-competitive selective pricing (Uber
Techs, 2020)

5. Discrimination
Increase feasibility and profitability of discrimination
(Cheng and Nowag, 2022)
Better ability to calculate personal WTP
Lower costs of setting personalized prices
Platforms enable discrimination on retail level
Challenges existing rules (Gal and Rubinfeld, 2023)
Most laws do not prohibit discrimination to final consumer
Most laws based on single-market analysis. But what
happens when more firms can engage in such discrimination
and create externalities on WTP in other markets?

6. Algorithmic remedies
Competition-by-design: Changing the parts of the code that
create the anti-competitive effects
Benefits
– Goes to the root of the problem
– Test in lab
– Some cases are straightforward (e.g., predation)
No panacea for all
– Identifying rules that lead to such outcomes (AI)
– How would the alteration affect pro-competitive effects

7. Mandatory Sharing
• Learning algorithms embody the knowledge
from training data
• Training data unlawfully collected or exploited
• Mandatory sharing of the unlawfully obtained
comparative advantage (Gal and Petit, 2021)

8. Algorithms as
coordination facilitators

9. Methods of Digitalised Coordination:
(Ezrachi and Stucke, 2016)
Algorithms as tools in existing agreements
Hub and Spoke
Expert coded coordination
Unsupervised learned coordination
9

10. Hub and Spoke
Examples: Las Vegas Hotels (US); Uber (South Africa)
Image: Manzini (2019)

11. Relevant questions
• Where does the data come from? (RealPage, 2023)
– Should we treat historical data differently, since it enables rivals to
learn about the algorithms’ decisional parameters?
• Who is using the algorithm?
• What do users know about its use by others?
• Which barriers to coordination are reduced by the algorithm?
• What goal is the algorithm maximizing? (Harrington, 2021)
• Does the algorithm impose limitations on users? (Eturas, 2017)
– Sanction for deviation? (Ageras, 2020)
• Psychological effects? (RealPage, 2023)
• Offsetting considerations (Uber cases)
– that cannot be obtained in a less harmful way?

12. Deep learning

13. Challenges
Legal: Do they create an “agreement?”
Enforcement: What is the remedy?
13

14. Existing tools
Facilitating Practices/Plus factors (Gal, 2021)
• The adoption of a certain TYPE of algorithm
• Transparency of the algorithm to rivals
• Transparency of input and output data
• Time to clarify when a series of algorithmic acts can be
viewed as an agreement
Joint/shared monopoly
• No need for an agreement, focus on conduct

15. Focus on Conduct/Outcome
Benefits (Calvano et al 2020)
• We can “read the mind” of the algorithm
• Goes to the root of the problem
Challenges
• Change the law
– Broad market inquiry tools (Motta et al 2022)?
• How to fashion the remedy (Gal, 2021)
– Which information should the algo ignore?
– Is it different from our practical inability to prevent human-created
oligopolistic coordination?
– If require to price only based on costs, is this not price regulation?

16. Algorithms and Merger policy

17. Combatting Coordination
• Mergers will become redundant in some coordination cases
– Yet in some cases become more important(RealPage 2017)
• Affect reporting thresholds
– Structural indicators less effective (HHI)
• Affect Analysis
– Weight of asymmetry (Coutts, 2021)
– Conglomerate mergers with multi-market contacts (Donini)
• Structural remedies less effective (Coutts, 2021)
• Limit algorithmic transparency in due diligence (Coutts, 2021)

18. Combatting Abuse
• Last resort in some cases
• Affect tools and thresholds
– SSNIP where prices not homogenous (Li et al., 2021)
– Monopolistic competition (McSweeney and O’dea, 2017)
• Affect Analysis? Examples:
– Control of resources (e.g., digital profiles)
– Enable third-parties to manipulate the algorithms?
– Potential of other algorithms to act as counterweights?
– How more widespread abuse affects welfare?
– Learning algorithms: Extending the timeframe
• Remedies that take advantage of algorithms (e.g., sharing, limiting)
Gal and Rubinfeld, “Algorithms, AI, and Mergers,” Antitrust L. J. (2023)

19. Some Resources
• Ezrachi and Stucke, Virtual Competition (2016)
• Ezrachi and Stucke, “Artificial Intelligence & Collusion: When Computers
Inhibit Competition,” University of Illinois L. Rev. (2017)
https://ssrn.com/abstract=2591874
• Gal, “Algorithms as Illegal Agreements”” Berkeley Tech. L. J. (2018)
https://ssrn.com/abstract=3171977
• Calvano et al., “Artificial Intelligence, Algorithmic Pricing and Collusion”
(2019) https://ssrn.com/abstract=3304991
• Manzini, “Collusion And Antitrust: The Dark Side Of Pricing Algorithms”
(2019), https://www.associazioneantitrustitaliana.it/wp-
content/uploads/2020/10/Tesi-Elena-Donini.pdf
• Leslie, “Algorithmic Predation,” NYU Law Rev. (2023)
• Nowag and Cheng, “Algorithmic Predation and Exclusion,” Penn. J. Bus. L.
(2023)
• Gal, “Limiting Algorithmic Coordination” Berkeley Tech. L. J. (2022)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4063081
• Gal and Rubinfeld, “Algorithms, AI and Mergers” Antitrust L. J. (2023)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4469586

20. Slides for third part of the discussion

21. Use of AI by authorities
• Computational antitrust (Schrepel; Groza;…)
• Evaluators, predictors, and illuminators of solutions
• Lead to more focused informational requirements
• More complex yet realistic models of dynamics
• Testing in the lab
• Challenge: A learning algorithm changes over time

22. Institutional Agility
• Creation of Data units
– Determine types and capabilities
– adaptive solutions
– on what side to we prefer to err
– Data-related ethical issues
• Interdisciplinary working groups
– Commonality of issues
– Inter and intra-agency (e.g., Financial regulator)
– Exploring alternative forms of AI regulation

23. • Obtaining relevant information:
– Protection of privacy and trade secrets
• Use of data-trusts? (Mahari, Lera and Pentland, 2021)
• Burdens of proof
– Precision of AI (+explainability)
– Reverse engineering Constructed algorithms
Assumptions regarding harm shift burden