Expert Tribunal Models For AI-Driven Antitrust Disputes

1. Introduction

Expert Tribunal Models for AI-Driven Antitrust Disputes refer to adjudicatory structures in which competition-law disputes involving artificial intelligence are decided with the assistance of judges, economists, data scientists, engineers, AI specialists, and other technical experts. The underlying idea is that conventional judicial processes may struggle with disputes involving algorithmic pricing, machine-learning models, automated decision-making, foundation models, cloud-compute concentration, data access, autonomous agents, algorithmic collusion, and AI-enabled exclusionary conduct.

AI-driven antitrust disputes are distinctive because the legally relevant conduct may be buried inside technical systems. A tribunal may therefore need to determine not merely what a company did, but:

• how an algorithm was designed and trained;
• what data it used;
• whether the model was capable of learning from competitors;
• whether pricing or ranking decisions were autonomous;
• whether human employees exercised meaningful control;
• whether apparently independent algorithms produced coordinated outcomes;
• whether access to compute, data, APIs or models constitutes a competitive bottleneck;
• whether an AI system created foreclosure effects;
• whether an algorithmic outcome was intentional, foreseeable or merely emergent; and
• whether a proposed remedy can actually be implemented and audited technically.

An expert tribunal model can therefore combine legal adjudication + economic analysis + technical expertise + evidentiary examination + continuing monitoring.

2. Why AI Creates a Need for Expert Tribunal Models

Traditional antitrust adjudication generally relies upon familiar evidence:

1. contracts;
2. prices;
3. market shares;
4. internal communications;
5. business documents;
6. economic evidence; and
7. witness testimony.

AI markets introduce additional evidence:

• source code;
• model architecture;
• training datasets;
• model weights;
• prompts and system instructions;
• API logs;
• telemetry;
• reinforcement-learning records;
• model cards;
• safety evaluations;
• algorithmic experimentation records;
• recommendation outputs;
• automated pricing histories;
• cloud-resource allocation records;
• inference costs;
• A/B testing results; and
• machine-generated communications.

The tribunal must consequently understand both legal causation and computational causation.

A useful conceptual formula is:

AI Antitrust Adjudication = Competition Law + Economics + Computer Science + Data Governance + Technical Evidence

3. Meaning of an Expert Tribunal

An expert tribunal is not necessarily a tribunal composed entirely of scientists.

A preferable structure is a multidisciplinary adjudicatory panel consisting of:

A. Legal member

Responsible for:

• statutory interpretation;
• procedural fairness;
• jurisdiction;
• burden of proof;
• admissibility;
• due process;
• proportionality; and
• final legal conclusions.

B. Competition economist

Responsible for:

• market definition;
• market power;
• counterfactual analysis;
• foreclosure;
• pricing effects;
• efficiencies;
• merger effects;
• entry barriers; and
• competitive harm.

C. AI/technical expert

Responsible for:

• algorithmic architecture;
• model functionality;
• training processes;
• autonomy;
• explainability;
• system dependencies;
• technical interoperability; and
• feasibility of remedies.

D. Data specialist

Responsible for:

• data access;
• data portability;
• data quality;
• data advantages;
• interoperability;
• privacy constraints; and
• data-driven network effects.

E. Sector specialist

Potentially relevant where AI is used in:

• financial markets;
• healthcare;
• transport;
• energy;
• telecommunications;
• defence;
• cloud computing; or
• digital advertising.

4. Possible Expert Tribunal Models

Model I — Permanent AI Competition Tribunal

A permanent specialist tribunal could hear disputes involving:

• AI platforms;
• foundation models;
• algorithmic pricing;
• AI mergers;
• cloud/compute markets;
• digital advertising;
• automated marketplaces; and
• AI-enabled exclusionary conduct.

Advantages

It would develop institutional expertise and avoid repeatedly educating generalist judges about AI technologies.

Risks

A permanent specialist tribunal could become excessively technical and potentially develop an overly interventionist approach.

5. Model II — Hybrid Competition Tribunal

A more practical model would combine:

Competition-law judge + economist + AI technical member.

The legal member would control the legal determination, while the expert members would provide specialized analysis.

This model is particularly useful where disputes involve complicated questions such as:

Did two supposedly independent pricing algorithms independently arrive at similar prices, or did their design create a mechanism through which they effectively coordinated?

The answer requires both legal and technical analysis.

6. Model III — Expert-Assisted General Court

Instead of creating a separate tribunal, ordinary courts could appoint independent technical experts.

The structure would be:

Court → competition expert → AI expert → independent technical report → adversarial submissions → judicial determination

This preserves conventional judicial authority while addressing technical complexity.

7. Model IV — Tribunal With Permanent Technical Secretariat

Another model would retain a conventional competition tribunal but establish a permanent technical unit containing:

• data scientists;
• AI engineers;
• economists;
• forensic programmers;
• cybersecurity specialists;
• computational economists; and
• data-governance experts.

The tribunal itself remains legally constituted, while the technical secretariat performs investigative and analytical functions.

This may be particularly suitable for complex digital competition litigation.

8. Model V — Tribunal + Regulatory Sandbox

An especially innovative model would permit the tribunal to order a controlled technical experiment.

For example, in an AI pricing dispute, the tribunal could require:

1. controlled removal of a particular algorithmic feature;
2. monitoring of prices;
3. comparison with the counterfactual;
4. measurement of consumer effects; and
5. independent verification.

This transforms antitrust adjudication from a purely retrospective exercise into a limited evidence-generating process.

However, such experimentation must remain subject to procedural fairness and cannot allow the tribunal to become both investigator and adjudicator without safeguards.

9. Model VI — Continuous-Review AI Tribunal

AI systems evolve rapidly. A remedy imposed today may become obsolete within months.

A specialist tribunal could therefore impose:

• periodic reporting;
• algorithmic audits;
• independent testing;
• compliance monitoring;
• interoperability assessments;
• access reviews; and
• periodic reconsideration of remedies.

This would be particularly relevant for dominant AI platforms whose models, APIs and business practices continuously change.

10. Key Legal Questions for an AI Expert Tribunal

The tribunal would need to resolve several new categories of questions.

A. Attribution

Who is responsible for an AI-generated anticompetitive outcome?

• developer?
• deployer?
• platform?
• user?
• cloud provider?
• autonomous agent?

B. Intent

Must the authority prove human intention?

AI systems may produce coordinated or exclusionary outcomes without an explicit instruction to do so.

C. Causation

Did the algorithm actually cause the competitive harm?

D. Foreseeability

Was the outcome reasonably foreseeable when the system was designed?

E. Market power

Does control over:

• data,
• compute,
• models,
• APIs,
• distribution,
• app stores, or
• AI infrastructure

create durable market power?

F. Remedy feasibility

Can a proposed remedy technically be implemented without destroying legitimate efficiencies?

11. Case Law

Because there are relatively few reported decisions dealing directly with AI-specific antitrust tribunals, existing competition cases provide the doctrinal foundation for designing such institutions.

Case 1 — United States v. Microsoft Corp., 253 F.3d 34 (D.C. Cir. 2001)

The Microsoft litigation is foundational for understanding technologically complex competition disputes.

The case concerned Microsoft's dominance in operating systems and its conduct concerning Internet Explorer and complementary technologies.

Relevance to AI tribunals

The litigation demonstrated that courts dealing with rapidly evolving technology must understand:

• technical architecture;
• platform dependencies;
• interoperability;
• network effects;
• software integration; and
• technological barriers to entry.

An AI tribunal would encounter substantially more complex versions of these questions.

Principle

Competition adjudication involving technological platforms requires an understanding of how the technology functions, not merely the contractual form of the conduct.

12. Case 2 — European Commission v. Google LLC (Google Shopping)

The European Commission's Google Shopping decision and subsequent judicial litigation are highly relevant to AI-driven competition disputes.

The controversy concerned Google's preferential positioning of its own comparison-shopping service.

Relevance

AI platforms increasingly determine:

• search rankings;
• recommendations;
• product visibility;
• advertising placement; and
• information access.

An expert tribunal could therefore use the Google Shopping experience as a model for examining whether algorithmic ranking systems discriminate in favour of affiliated services.

Key lesson

The tribunal may need to examine the architecture and actual operation of ranking algorithms, rather than relying exclusively on written policies.

13. Case 3 — Google LLC v. Commission, Case T-612/17

The General Court's Google Shopping judgment provides an important example of judicial scrutiny of complex digital-platform conduct.

The case illustrates the difficulty of distinguishing:

• legitimate product improvement;
• competition on the merits; and
• exclusionary leveraging by a dominant platform.

AI relevance

The same problem arises where an AI platform:

• ranks its own AI service first;
• gives privileged API access to affiliated products;
• integrates its own foundation model into distribution channels; or
• uses platform data to improve its competing AI product.

An expert tribunal could employ technical evidence to determine whether the conduct represents ordinary innovation or anticompetitive self-preferencing.

14. Case 4 — United States v. Apple Inc.

The U.S. antitrust litigation concerning Apple's ecosystem provides an important model for disputes involving integrated technological platforms.

The central issues include:

• ecosystem control;
• distribution restrictions;
• interoperability;
• switching costs;
• access restrictions; and
• control over complementary products.

AI relevance

AI ecosystems increasingly combine:

model + cloud + API + application + distribution + data.

A dominant AI company could potentially use control at one layer to restrict competition at another.

An expert tribunal would therefore need to understand the technical and commercial relationships among different layers of the AI stack.

15. Case 5 — United States v. Google LLC, Search and Advertising Litigation

The Google search and advertising antitrust proceedings are particularly relevant to algorithmically mediated markets.

They demonstrate the importance of examining:

• default arrangements;
• distribution;
• scale;
• data advantages;
• network effects;
• advertising technology; and
• technological feedback loops.

AI relevance

AI search systems may produce even stronger feedback effects:

more users → more queries → more behavioural data → better models → better results → more users.

An expert tribunal could therefore investigate whether an AI platform's data and model advantages create self-reinforcing market power.

16. Case 6 — FTC v. Meta Platforms, Inc.

The FTC's litigation concerning Meta's acquisitions of Instagram and WhatsApp is important for understanding competition problems involving digital ecosystems and innovation.

AI relevance

AI acquisitions create particularly difficult questions concerning:

• potential competition;
• innovation competition;
• data accumulation;
• nascent competitors;
• future technological trajectories; and
• ecosystem expansion.

An expert tribunal could require technical evidence concerning whether an acquired AI company was a genuine competitive constraint or a potentially important future rival.

17. Case 7 — Intel Corp. v. European Commission, Case C-413/14 P

The Intel litigation is important for the treatment of rebates, foreclosure and economic evidence under Article 102 TFEU.

The Court of Justice emphasized the importance of examining the actual or potential exclusionary effects of conduct where the undertaking produces sufficient evidence to raise such issues.

AI relevance

AI markets frequently involve:

• volume discounts;
• compute rebates;
• cloud credits;
• API pricing;
• preferential access;
• bundled AI services; and
• capacity reservations.

An expert tribunal could therefore require sophisticated economic and technical evidence rather than treating the formal structure of an arrangement as determinative.

18. Case 8 — Commission v. United Brands, Case 27/76

Although much older, United Brands remains important for market power and dominance analysis.

It demonstrates the importance of identifying the competitive significance of a particular product or service and evaluating the economic conditions surrounding it.

AI relevance

The same analytical problem arises with:

• foundation models;
• GPU compute;
• AI inference;
• proprietary datasets;
• model-access APIs; and
• AI safety infrastructure.

An expert tribunal may need to determine whether a particular technological resource constitutes a distinct competitive bottleneck.

19. Case 9 — Bronner v. Mediaprint, Case C-7/97

Bronner is particularly relevant to essential-facility-type theories.

The Court adopted a restrictive approach to mandatory access to infrastructure controlled by a dominant undertaking.

AI relevance

Similar disputes could arise over:

• proprietary datasets;
• AI model interfaces;
• compute infrastructure;
• inference APIs;
• training infrastructure;
• cloud capacity; and
• model-distribution channels.

An AI expert tribunal could determine whether technical indispensability is genuine or merely asserted.

20. Case 10 — Slovak Telekom v Commission, Joined Cases C-152/19 P and C-165/19 P

This case is significant for refusal-to-deal and access-related competition analysis.

AI relevance

An AI platform might control an essential technological layer and restrict rivals' access to:

• APIs;
• interoperability tools;
• data;
• model outputs;
• compute resources; or
• technical interfaces.

The tribunal would need to distinguish legitimate security and quality-control restrictions from exclusionary conduct.

21. Expert Evidence Architecture

An AI tribunal should establish a specialized evidentiary framework.

Tier 1 — Documentary evidence

• contracts;
• policies;
• emails;
• technical specifications;
• internal presentations.

Tier 2 — Computational evidence

• source code;
• model logs;
• API records;
• training records;
• model versions;
• system prompts.

Tier 3 — Economic evidence

• market shares;
• price effects;
• diversion ratios;
• entry analysis;
• switching costs;
• counterfactual modelling.

Tier 4 — Experimental evidence

• A/B tests;
• controlled algorithmic experiments;
• simulated market conditions;
• counterfactual model runs.

Tier 5 — Expert testimony

Independent experts should explain the evidence in a manner understandable to the legal members.

22. Algorithmic Forensics

One of the most important functions of an AI tribunal would be algorithmic forensics.

This could involve reconstructing:

1. the algorithm's objective;
2. its available information;
3. its decision rules;
4. its learning process;
5. its interaction with competing systems;
6. changes in its behaviour over time; and
7. the causal connection between the system and market outcomes.

For example:

Two competitors may use independent AI pricing systems that repeatedly converge on similar prices.

The tribunal must determine whether this represents:

• conscious coordination;
• algorithmic facilitation;
• hub-and-spoke coordination;
• tacit coordination;
• independent optimization; or
• an innocent common response to market conditions.

23. Burden of Proof

AI disputes create a serious information asymmetry.

The company often possesses:

• source code;
• model logs;
• training data;
• internal experimentation records;
• model evaluations; and
• technical documentation.

The regulator may possess only external market observations.

An expert tribunal could therefore adopt carefully controlled disclosure mechanisms.

Possible structure

Initial regulator showing → targeted disclosure → independent technical examination → rebuttal → final adjudication

This would prevent both:

• excessive secrecy by dominant firms; and
• unjustified disclosure of commercially sensitive information.

24. Confidentiality and Trade Secrets

AI litigation may involve extraordinarily sensitive information.

Disclosure of complete model weights or source code may itself create competitive harm.

Therefore, expert tribunals could use:

• confidentiality rings;
• secure data rooms;
• independent experts;
• restricted-access technical annexes;
• source-code escrow;
• redacted public decisions; and
• cryptographic verification.

The objective should be meaningful scrutiny without unnecessary disclosure.

25. Independent Technical Experts

The tribunal should avoid simply accepting the dominant firm's own technical explanation.

Independent experts should be:

• appointed by the tribunal;
• institutionally separate from the parties;
• subject to disclosure obligations;
• cross-examinable where appropriate; and
• required to explain assumptions and methodology.

This is particularly important where a company claims:

“The AI system made the decision autonomously.”

The tribunal must determine whether “autonomy” is technically meaningful or merely a mechanism for avoiding legal responsibility.

26. Expert Tribunal and Algorithmic Collusion

AI pricing presents one of the most difficult areas.

Suppose:

Firm A's algorithm → observes market → raises price

and

Firm B's algorithm → observes market → raises price.

Repeatedly, the algorithms converge.

The tribunal must distinguish:

Scenario 1 — Independent adaptation

No agreement and no prohibited coordination.

Scenario 2 — Explicit coordination

Algorithms execute an unlawful agreement.

Scenario 3 — Algorithmic facilitation

A human or platform creates a system deliberately designed to facilitate coordination.

Scenario 4 — Autonomous collusion

Independent systems learn a mutually beneficial strategy without direct human communication.

The fourth category creates significant doctrinal challenges because conventional antitrust concepts often focus on agreement, intention or concerted practice.

27. AI Tribunal and Merger Control

Expert tribunals could be particularly valuable in AI merger cases.

A tribunal might evaluate:

Innovation competition

Would the target have developed:

• a competing foundation model?
• a new architecture?
• a cheaper inference system?
• an alternative AI ecosystem?

Data effects

Would the merger combine datasets that create an insurmountable data advantage?

Compute effects

Would the merger increase control over scarce computing resources?

Ecosystem effects

Would the merged firm control:

cloud → model → API → application → distribution?

28. Remedies

An expert tribunal should possess technologically sophisticated remedies.

Possible remedies include:

Structural remedies

• divestiture;
• separation of AI business units;
• infrastructure separation.

Behavioural remedies

• interoperability;
• non-discrimination;
• API access;
• data portability;
• switching mechanisms.

Algorithmic remedies

• modification of ranking systems;
• removal of discriminatory parameters;
• independent algorithm audits.

Governance remedies

• independent compliance monitors;
• technical trustees;
• model-access committees.

Dynamic remedies

• periodic reassessment;
• sunset clauses;
• automatic review triggers.

29. The Role of AI Technical Monitors

A particularly innovative mechanism would be an AI Competition Monitor.

The monitor could periodically test:

• prices;
• ranking;
• API access;
• interoperability;
• latency;
• discrimination;
• model availability;
• switching costs.

The monitor would report to the tribunal rather than to the company.

This would make remedies verifiable rather than merely declaratory.

30. Advantages of Expert Tribunal Models

1. Technical competence

The tribunal understands sophisticated AI systems.

2. Faster adjudication

Repeated technical education of generalist judges can be reduced.

3. Better economic analysis

Economists can assess complex network effects and counterfactuals.

4. Better remedies

Technical experts can determine whether remedies are practically implementable.

5. Institutional consistency

A specialist tribunal can develop coherent AI competition jurisprudence.

6. Evidence preservation

Specialized procedures can address rapidly changing AI systems and disappearing logs.

31. Risks

Expert tribunals also present serious dangers.

A. Technocracy

Technical expertise must not replace legal judgment.

B. Capture

Experts may have prior relationships with:

• technology companies;
• regulators;
• consultancies;
• universities; or
• AI laboratories.

Strict conflict-of-interest rules are therefore necessary.

C. Opacity

A decision based upon a complex technical model may become impossible for ordinary parties to understand.

D. Procedural unfairness

A party must have a genuine opportunity to challenge expert evidence.

E. Regulatory overreach

The tribunal should not become an industrial-policy institution.

32. Recommended Institutional Design

A balanced model would be a Hybrid AI Competition Tribunal:

The legal member should retain final responsibility for the legal judgment, while technical members provide transparent, contestable expertise.

33. Procedural Flow

Complaint / Investigation

↓

Preliminary jurisdiction and competition assessment

↓

Technical preservation order

↓

Secure collection of algorithmic evidence

↓

Economic analysis

↓

Independent AI forensic examination

↓

Expert reports

↓

Cross-examination / adversarial submissions

↓

Legal + economic + technical deliberation

↓

Final decision

↓

Remedy

↓

Technical monitoring

↓

Periodic review

This creates a continuous but procedurally controlled model of AI competition adjudication.

34. Relationship With Conventional Courts

Expert tribunals should not necessarily replace ordinary courts.

A better architecture is:

Competition Authority
↓
Expert AI Competition Tribunal
↓
Specialist Appellate Court / General Court
↓
Supreme or Constitutional Review where appropriate

This preserves judicial review while allowing specialist first-instance adjudication.

35. Broader Competition-Law Significance

Expert tribunals could change the nature of antitrust adjudication.

Traditional antitrust often asks:

What conduct occurred and what was its economic effect?

AI competition law increasingly requires:

What computational process generated the conduct, who controlled that process, how did it affect the competitive structure, and can the technical mechanism itself be modified?

This represents a movement from document-centric antitrust toward system-centric antitrust.

36. Conclusion

Expert Tribunal Models for AI-Driven Antitrust Disputes provide a potentially valuable institutional response to the increasing technical complexity of digital competition.

The strongest model is not a tribunal dominated by technologists. It is a hybrid adjudicatory institution combining:

• judicial independence;
• competition-law expertise;
• economic analysis;
• AI engineering;
• data science;
• technical forensics; and
• continuous remedy monitoring.

The jurisprudence of Microsoft, Google Shopping, Intel, United Brands, Bronner, Slovak Telekom, Apple and Meta demonstrates that modern competition disputes increasingly require courts and regulators to understand technological architecture, network effects, data advantages and innovation incentives.