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EU Digital Markets Act (DMA) Self-Preferencing: Compliance Examples

Design ranking and discovery systems that don't favour first-party services.

Focused on Article 6(5): ranking/indexing/crawling fairness + the evidence you need to prove it.

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Author

Sorena AI

Published

Feb 21, 2026

Updated

Feb 23, 2026

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Publication metadata

Sorena AI

Published Feb 21, 2026

Updated Feb 23, 2026

Overview

DMA Article 6(5) prohibits gatekeepers from treating their own services and products more favourably in ranking and related indexing/crawling than similar third-party services. Compliance is not just "remove a banner" - it requires ranking governance, experimentation controls, bias testing, and clear documentation of how ranking parameters are applied.

Section 1

What the DMA requires (Article 6(5))

Article 6(5) requires that the gatekeeper must not treat more favourably, in ranking and related indexing/crawling, services and products offered by the gatekeeper itself than similar services or products of a third party.

It also requires transparent, fair, and non-discriminatory conditions for ranking. That implies governance, explainability, and repeatable testing.

Scope includes: search results, app store listings, marketplace listings, recommendations, "top picks", auto-suggest, and any surface where ranking or visibility is algorithmically controlled.
"Related indexing and crawling" matters for web and app discovery: coverage and freshness decisions can create de facto preference.
You need a defensible definition of "similar services/products" for each CPS context.

Section 2

Common self-preferencing patterns (and why they fail)

Self-preferencing often hides in "defaults" and "UX shortcuts": pre-installed placements, privileged UI real estate, or algorithmic boosts that are not visible in policy docs.

If you cannot explain and reproduce your ranking outcomes, you cannot reliably prove non-discrimination.

Hard-coded boosts for first-party services (e.g., multipliers, whitelists, hidden quality overrides).
Preferential UI treatment: pinned cards, "recommended by platform" labels, default filters favouring first-party categories.
Asymmetric eligibility rules: third parties face stricter review criteria, slower indexing, or higher friction for the same distribution outcome.

Section 3

Compliance design patterns that work (engineering + governance)

The goal is not "no ranking" - it is ranking neutrality with consistent rules and measurable checks.

Build a ranking governance model: parameter registry, change control, experiment approvals, and periodic bias testing.

Parameter registry: maintain a documented list of ranking features and how they are applied to first-party vs third-party items.
Eligibility parity: define consistent inclusion/exclusion rules and apply the same verification thresholds to first-party and third-party items.
Testing: run periodic "swap tests" and counterfactual checks to detect first-party advantage not explained by neutral features.
Transparency: publish a high-level ranking explanation that is stable across changes and does not rely on vague claims.

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Section 4

Examples by CPS: app stores, search, marketplaces, and social discovery

Use these examples as prompts to review your own ranking and discovery surfaces. For each, record the ranking rules, the UX treatment, and the evidence you can produce.

The same pattern can show up in different CPS: a "featured" slot in an app store is structurally similar to a "top results" carousel in search.

App store: first-party apps placed above third-party apps for identical queries; first-party apps exempt from certain eligibility requirements; faster indexing for first-party updates.
Search: first-party vertical modules (shopping/maps/video) persistently outrank third-party results without neutral relevance justification; crawling/indexing coverage disfavors third-party categories.
Marketplace/intermediation: first-party offers receive better placement or badges; default sort prioritizes first-party fulfilment or services without clear user-choice toggles.
Social discovery: recommendation systems favour first-party services or formats; privileged distribution for first-party content or features.

Section 5

Evidence checklist (Article 11-style) for self-preferencing compliance

Self-preferencing compliance is easiest to defend when you can show: rules -> implementation -> outcomes -> monitoring.

The Commission's Article 11 reporting template expects detailed explanations and supporting data. Use the evidence checklist below to avoid "trust us" compliance.

Ranking documentation: parameter registry, eligibility rules, indexing/crawling policies, and change logs.
Outcome evidence: benchmark queries/tests showing parity, with statistical analysis and "why it ranked" explanation.
Experiment governance: approvals, guardrails, and monitoring for ranking changes.
Remediation playbook: how you respond when tests detect first-party advantage.

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