AI-Controlled Quality Assurance Ecosystems and Certification Power

Introduction

AI-controlled quality assurance (QA) ecosystems refer to systems in which artificial intelligence is used to inspect, test, score, certify, audit, validate, or continuously monitor products, services, suppliers, professionals, or compliance processes. When one AI platform controls a substantial part of the certification ecosystem, it may acquire certification power: the ability to determine who receives approval, what standards are applied, what evidence is accepted, and whether access to a market or platform continues.

From a competition-law perspective, the central concern is not AI itself. The concern arises when control over certification becomes a mechanism for excluding rivals, discriminating between trading partners, tying complementary services, raising competitors' costs, or creating an unavoidable technological bottleneck.

1. Meaning of AI-Controlled Quality Assurance

An AI-controlled QA ecosystem may perform several functions:

1. Automated inspection – computer vision or machine learning detects defects.
2. Supplier scoring – algorithms rank suppliers according to predicted quality.
3. Compliance verification – AI checks whether businesses satisfy regulatory or contractual standards.
4. Certification decisions – automated systems determine whether a product or supplier passes.
5. Continuous monitoring – certification becomes conditional upon ongoing algorithmic monitoring.
6. Risk classification – AI assigns risk scores that determine inspection intensity.
7. Digital credentials – AI-generated certificates or compliance credentials are used to obtain market access.
8. Platform access control – marketplaces, app stores, payment systems, cloud platforms or procurement systems may rely upon AI-generated quality scores.

The competitive significance increases where the AI-controlled system is operated by a firm that also competes with the businesses being evaluated.

2. Certification Power as a Competition-Law Problem

Certification can become commercially indispensable.

For example:

AI Certification Platform → Quality Standard → Supplier Certification → Platform Access → Customers

If the certification platform becomes indispensable, exclusion from the certification system can effectively mean exclusion from the market.

A competition authority may therefore examine:

• whether the certification system is genuinely independent;
• whether certification criteria are transparent;
• whether rivals receive equivalent access;
• whether certification is objectively necessary;
• whether the platform uses certification data to compete against certified firms;
• whether certification is bundled with another service;
• whether certification fees are discriminatory;
• whether an incumbent uses certification requirements to protect its own downstream business.

3. Relevant Competition-Law Theories

A. Abuse of Dominance

If an AI certification provider occupies a dominant position, discriminatory or exclusionary certification practices may constitute an abuse of dominance.

Potential conduct includes:

• arbitrary refusal to certify;
• discriminatory scoring;
• excessive certification requirements imposed on rivals;
• preferential certification of affiliated businesses;
• sudden modification of algorithmic standards;
• denial of access to essential certification infrastructure.

The relevant market could potentially be:

• AI quality-assurance software;
• certification services;
• digital compliance verification;
• a particular technical certification;
• platform-based quality verification.

Market definition remains fact-dependent.

4. Self-Preferencing Through AI Certification

One particularly important problem occurs where the certifier is also a market participant.

For example:

Platform A

→ operates AI quality-certification system
→ certifies suppliers
→ sells its own products
→ controls marketplace ranking.

The platform could potentially design certification criteria that systematically favour its own products.

This creates a vertical conflict of interest.

The competition-law question becomes whether certification is being used as an instrument of exclusion rather than a genuine quality-control mechanism.

5. Algorithmic Discrimination

AI certification systems may discriminate even without an explicit discriminatory instruction.

Possible mechanisms include:

• training-data bias;
• different error rates;
• historical supplier data;
• geographic bias;
• language bias;
• different treatment of new entrants;
• algorithmic risk thresholds;
• opaque scoring variables.

Competition concerns arise when such differences translate into unequal commercial access.

For example, if smaller suppliers repeatedly receive lower AI quality scores despite comparable objective quality, certification requirements could raise their costs and disadvantage them against established firms.

6. Certification as an Essential Facility

Certification may become economically indispensable when:

• customers require the certificate;
• regulators recognize only particular certifications;
• procurement contracts require certification;
• marketplaces refuse uncertified suppliers;
• insurance companies require certification;
• payment or financing providers rely upon certification.

Where the certification infrastructure is practically unavoidable, refusal of access can potentially raise essential-facility or refusal-to-deal issues, subject to the demanding legal requirements applicable in the relevant jurisdiction.

The important distinction is between:

ordinary commercial certification

and

certification infrastructure that competitors cannot realistically replicate.

7. Tying and Bundling

An AI certification provider may require businesses to purchase another service.

For example:

AI Certification + Cloud Hosting
AI Certification + Cybersecurity Monitoring
AI Certification + Payment Processing
AI Certification + Data Analytics

If certification is indispensable and the provider possesses market power, compulsory purchasing of the tied product can potentially exclude competing suppliers.

The analysis generally considers:

1. distinct products;
2. market power;
3. coercion or practical compulsion;
4. foreclosure;
5. objective justification and efficiencies.

8. Certification Data as a Competitive Advantage

AI certification generates valuable information concerning:

• defect rates;
• production processes;
• supplier reliability;
• manufacturing capacity;
• compliance weaknesses;
• customer complaints;
• product performance.

A vertically integrated certification provider may therefore obtain competitively sensitive information about rivals.

The danger is particularly serious where the certifier subsequently competes with those same businesses.

Potential concerns include:

• information exploitation;
• exclusionary use of data;
• discriminatory treatment;
• leveraging;
• self-preferencing.

9. Interoperability and Portability

A certification ecosystem can create lock-in when certificates are stored in a proprietary technical format.

A supplier might have:

10 years of certification history → Platform A

but cannot transfer that history to:

Platform B.

This may increase switching costs and make competing certification platforms less attractive.

Competition authorities may therefore examine:

• interoperability;
• data portability;
• API access;
• credential portability;
• recognition of competing certificates.

10. Algorithmic Certification and Due Process

Certification decisions may be automated.

Suppose an AI system rejects a supplier:

"Certification denied — risk score 87/100."

If the supplier cannot determine:

• what caused the score;
• what evidence was considered;
• how to challenge the decision;
• whether the data were accurate;
• whether competing firms are evaluated under the same criteria,

the certification system can become an opaque commercial gatekeeper.

From a competition perspective, opacity becomes particularly problematic when the certifier controls access to an important market.

11. Relevant Case Laws

The following cases provide useful legal analogies for understanding AI-controlled certification power.

1. United Brands v Commission

Case 27/76, United Brands v Commission

The European Court of Justice examined dominance, discriminatory conditions and exclusionary conduct.

Relevance

An AI certification platform with substantial market power could potentially face scrutiny if it imposes materially different certification conditions on equivalent businesses without objective justification.

The case is important for understanding:

• dominance;
• discriminatory treatment;
• market power;
• commercial dependence.

2. Commercial Solvents v Commission

Joined Cases 6/73 and 7/73, Commercial Solvents v Commission

The Court addressed the use of market power in one market to restrict competition in another related market.

Relevance

An AI certification provider controlling an indispensable upstream certification service could potentially leverage that position into a downstream market.

For example:

Certification infrastructure → downstream marketplace

If certification is manipulated to disadvantage competing downstream businesses, the conduct may raise leveraging concerns.

3. Bronner v Mediaprint

Case C-7/97, Oscar Bronner GmbH & Co. KG v Mediaprint

The Court established demanding conditions for treating infrastructure as an indispensable facility for purposes of refusal-to-deal analysis.

Relevance

The case is particularly relevant to AI certification infrastructure.

A claimant would generally need to demonstrate more than inconvenience. The infrastructure must satisfy stringent indispensability requirements.

Thus, not every popular AI certification system is automatically an essential facility.

4. IMS Health v Commission

Case C-418/01 P, IMS Health GmbH & Co. OHG v Commission

The case concerned access to an indispensable information structure and the exceptional circumstances under which refusal to license could constitute an abuse.

Relevance

AI certification databases can become extremely valuable information infrastructures.

If a proprietary certification database becomes indispensable for competitors, questions may arise concerning:

• access;
• interoperability;
• licensing;
• replication;
• refusal to provide information.

The case demonstrates that indispensability remains a critical threshold.

5. Microsoft Corp. v Commission

Case T-201/04, Microsoft Corp. v Commission

The General Court examined Microsoft's refusal to provide interoperability information and related exclusionary effects.

Relevance

The case provides a strong analogy for AI certification interoperability.

A dominant certification platform could potentially create competitive concerns if it:

• refuses necessary API access;
• prevents competing certification systems from interoperating;
• restricts portability of certification data;
• makes alternative certification systems technically incompatible.

6. Google Shopping

Case AT.39740, Google Search (Shopping)

The European Commission examined Google's treatment of its own comparison-shopping service within its general search results.

Relevance

The case is relevant to AI self-preferencing.

An AI-controlled quality platform that:

1. certifies third-party businesses;
2. operates a marketplace; and
3. gives preferential certification or visibility to its own affiliated businesses

could raise analogous questions concerning preferential treatment and competitive foreclosure.

The legal analysis would depend on the precise market structure and conduct.

7. Slovak Telekom

Case C-165/19 P, Slovak Telekom v Commission

The case concerned exclusionary conduct involving access to telecommunications infrastructure.

Relevance

It demonstrates the importance of access conditions where an infrastructure provider possesses significant market power.

An analogous AI certification infrastructure could raise competition concerns if access conditions are designed to disadvantage competing certification providers.

8. Servizio Elettrico Nazionale

Case C-377/20, Servizio Elettrico Nazionale

The Court examined exclusionary abuse and the distinction between competition on the merits and conduct capable of restricting competition.

Relevance

This is particularly useful for AI systems because a dominant company can legitimately improve its certification technology.

The competition concern is not simply:

"AI system is better."

The relevant question is whether the dominant undertaking uses legitimate competitive advantages or instead deploys its position to foreclose equally efficient competitors.

12. Certification Power and Raising Rivals' Costs

An AI certification incumbent could theoretically raise rivals' costs by:

• imposing expensive testing requirements;
• requiring proprietary equipment;
• demanding repeated certification;
• changing technical standards frequently;
• withholding certification data;
• charging competitors higher certification fees.

This may produce a raising-rivals'-costs strategy.

The effect can be especially substantial where customers will not purchase from uncertified suppliers.

13. Dynamic Standards and Competitive Exclusion

AI certification standards can evolve rapidly.

This creates an unusual competition problem.

A dominant certification provider could repeatedly introduce new requirements:

Version 1 → Version 2 → Version 3 → Version 4

If each new standard requires substantial investment in proprietary technology, smaller competitors may be unable to keep up.

However, technological improvement is not inherently anticompetitive.

The relevant distinction is between:

Legitimate innovation

A new standard genuinely improves:

• safety;
• reliability;
• accuracy;
• cybersecurity;
• consumer protection.

Potential exclusion

A new standard primarily functions to:

• exclude competing suppliers;
• favour affiliated products;
• prevent interoperability;
• increase switching costs.

14. Certification Fees and Excessive Pricing

Where an AI certification platform possesses significant market power, unusually high certification fees may raise concerns.

Potential indicators include:

• large margins;
• absence of competitive alternatives;
• mandatory certification;
• high switching costs;
• regulatory dependence.

However, excessive-pricing analysis is legally complex and jurisdiction-specific.

High prices alone do not establish an infringement.

15. AI Certification and Network Effects

Certification ecosystems may develop strong network effects:

More certified suppliers

↓

More customers use the system

↓

More businesses seek certification

↓

More certification data

↓

Better AI model

↓

More accurate certification

↓

More customers

This creates a feedback loop.

Once established, competing certification providers may find it difficult to attract customers because they lack the incumbent's historical dataset.

This can produce data-driven entry barriers.

16. Certification Ecosystems and Mergers

Competition authorities may also examine acquisitions involving:

• AI QA startups;
• testing laboratories;
• certification bodies;
• compliance-data platforms;
• supplier-rating platforms.

A merger could eliminate an emerging competitor before it becomes a significant competitive constraint.

Relevant theories may include:

• horizontal overlap;
• vertical foreclosure;
• data concentration;
• elimination of potential competition;
• interoperability concerns.

17. Regulatory Standards Versus Private Certification

Not all certification power is commercial.

Governments may establish mandatory standards for:

• aviation;
• pharmaceuticals;
• medical devices;
• automobiles;
• food safety;
• cybersecurity;
• financial services.

Private certification can supplement such standards.

Competition concerns become more complex where a private AI certifier effectively performs a regulatory function without adequate safeguards.

The distinction should therefore be maintained between:

public regulatory certification

and

private commercially controlled certification.

18. Remedies

Competition authorities could potentially consider several remedies.

Structural remedies

• separation of certification and downstream commercial activities;
• divestiture of certification assets;
• separation of data operations.

Behavioural remedies

• nondiscriminatory certification;
• transparent criteria;
• independent auditing;
• appeal mechanisms;
• access obligations;
• fair certification fees.

Technical remedies

• API interoperability;
• portability of certification credentials;
• standardized data formats;
• independent algorithmic testing.

Data remedies

• restrictions on use of competitor data;
• data-access requirements;
• purpose limitation;
• separation between certification and commercial datasets.

19. Compliance Framework for AI Certification Platforms

A competition-compliant AI certification ecosystem should ideally incorporate:

1. Objective certification criteria
2. Equal treatment of comparable applicants
3. Documented algorithmic standards
4. Independent review mechanisms
5. Human appeal procedures
6. Interoperability
7. Portable certification records
8. Clear fee structures
9. Separation of sensitive competitor information
10. Regular competition-law audits
11. Independent algorithmic testing
12. Restrictions on self-preferencing

20. Key Competition-Law Risks

21. Core Legal Principle

The central competition-law distinction is:

AI may legitimately improve quality assurance, but control over quality assurance should not become an artificial mechanism for controlling competitive access to the market.

A dominant undertaking can generally compete through better technology, better testing and better certification. Competition concerns become stronger where the certification mechanism is used to exclude competitors rather than objectively measure quality.

Conclusion

AI-controlled QA ecosystems are increasingly capable of functioning as private regulatory infrastructures. Their competitive importance arises when certification becomes indispensable for marketplace participation, procurement, financing, insurance, platform access, or consumer trust.

The principal competition-law issues include abuse of dominance, discriminatory certification, refusal of access, essential-facility questions, self-preferencing, tying, data foreclosure, interoperability restrictions, raising rivals' costs, and exclusionary acquisitions.