Dark Patterns and Competition Implications

1. Introduction

Dark patterns are interface designs or digital practices that intentionally or predictably steer users toward decisions they might not otherwise make. Examples include hidden cancellation processes, pre-selected options, deceptive countdowns, disguised advertisements, forced continuity, difficult privacy choices, and interfaces that make one option substantially easier to select than another.

Dark patterns are traditionally associated with consumer protection and unfair commercial practices, but they also have important competition-law implications. A dominant digital platform may use interface design to reinforce market power, raise switching costs, exploit data advantages, foreclose rivals, or favour its own services.

The competition concern therefore arises when dark patterns are not merely misleading but become a mechanism for acquiring, maintaining, or exploiting market power.

2. Relationship Between Dark Patterns and Competition Law

Dark patterns can affect competition through several mechanisms:

A. Increasing switching costs

A platform may make it easy to subscribe but difficult to cancel or migrate data.

This can:

• reduce customer mobility;
• increase retention artificially;
• discourage multi-homing;
• strengthen network effects;
• protect an incumbent from competitive pressure.

Where the undertaking is dominant, such conduct may potentially constitute an exclusionary abuse.

B. Self-preferencing

A platform can design its interface so that its own products or services receive:

• prominent placement;
• default status;
• preferential search ranking;
• additional recommendations;
• easier purchasing pathways.

The issue becomes particularly significant where competitors depend on the platform to reach customers.

The competition question is not simply whether the interface is manipulative. It is whether the design distorts competitive opportunities between the platform's own service and rival services.

C. Exploitative conduct

Dark patterns can exploit users who have limited ability to negotiate with a dominant platform.

Examples include:

• forced acceptance of contractual terms;
• confusing privacy choices;
• hidden charges;
• difficult cancellation;
• forced account creation;
• repeated consent requests.

Competition law may become relevant where such practices are connected with dominance, market power, or competitive foreclosure.

D. Data extraction

Many digital services operate on a zero monetary price.

Dark patterns may encourage users to disclose:

• location;
• contacts;
• browsing behaviour;
• purchasing information;
• biometric or behavioural information;
• advertising preferences.

The resulting data advantage can reinforce a platform's competitive position.

Thus, interface manipulation → additional data collection → improved targeting → stronger network effects → greater market power can become a competition concern.

3. Dark Patterns and Network Effects

Digital markets frequently exhibit strong network effects.

A platform becomes more valuable as more users, advertisers, sellers, developers, or content providers participate.

A dark pattern may therefore have effects beyond the individual transaction.

For example:

Manipulative onboarding → increased user acquisition → larger user network → more data → better advertising/product optimisation → greater attractiveness to advertisers → higher revenue → greater ability to invest → stronger market position.

Consequently, an apparently small interface practice can contribute to cumulative market foreclosure.

4. Dark Patterns and Consumer Lock-In

Lock-in becomes particularly relevant where a platform controls:

• data portability;
• account functionality;
• subscriptions;
• digital wallets;
• app ecosystems;
• cloud services;
• social graphs;
• digital identities.

A platform might make joining extremely simple while making departure substantially more difficult.

This creates an important distinction:

Ordinary product design
→ improves usability.

Potentially problematic design
→ systematically increases the friction associated with switching to competitors.

The latter may be relevant under theories of exclusionary abuse, tying, refusal to interoperate, or unfair contractual conditions, depending on the circumstances.

5. Dark Patterns and Tying

Dark patterns may facilitate tying by making acceptance of an additional service appear mandatory.

For example:

Core service → additional service presented as "recommended" → alternative hidden or difficult to locate → user accepts additional service.

If the undertaking has substantial market power, competition authorities may examine whether the interface effectively forces customers to purchase or use an adjacent product.

The legal analysis would normally require consideration of:

1. separate products;
2. market power;
3. coercion or practical compulsion;
4. foreclosure;
5. objective justification;
6. effects on competitors and consumers.

6. Dark Patterns and Default Bias

Defaults can have enormous competitive significance.

Users frequently accept default settings rather than actively changing them.

A dominant platform can therefore influence:

• search engines;
• browsers;
• payment services;
• advertising settings;
• app stores;
• digital assistants;
• shopping recommendations.

The competition concern increases when rivals cannot obtain equivalent access to the same default position.

7. Dark Patterns and Algorithmic Personalisation

Modern dark patterns may be algorithmically personalised.

Instead of presenting every user with the same interface, an algorithm can identify users who are:

• more likely to subscribe;
• less likely to cancel;
• more responsive to scarcity messages;
• highly price-sensitive;
• particularly susceptible to particular prompts.

The platform can then dynamically modify the interface.

This raises a competition issue because personalisation can transform a static interface practice into scalable algorithmic conduct.

It may also create information asymmetry between:

• platform and user;
• platform and competitor;
• regulator and platform.

8. Dark Patterns and Algorithmic Collusion

A particularly novel issue arises where competing platforms use similar optimisation systems.

Suppose several firms independently optimise interfaces for:

• conversion;
• retention;
• price acceptance;
• subscription renewal.

Their algorithms may discover similar strategies that systematically increase consumer friction or reduce cancellation.

This does not automatically establish an antitrust infringement. Competition law generally requires careful examination of:

• coordination;
• communication;
• common algorithms;
• information exchange;
• unilateral conduct;
• conscious parallelism;
• competitive effects.

The existence of similar dark patterns alone is not proof of a cartel.

9. Important Case Laws

1. FTC v. Amazon.com, Inc. — United States

The U.S. Federal Trade Commission's litigation concerning Amazon includes allegations relating to the design of Prime's subscription and cancellation processes.

The FTC alleged that Amazon used deceptive interfaces, including practices surrounding Prime enrollment and cancellation, to make consumers continue subscriptions.

Competition significance

The broader competition significance lies in the relationship between:

• platform power;
• consumer retention;
• interface design;
• subscription ecosystems.

It demonstrates why dark patterns can become relevant to a broader platform-market-power analysis.

2. FTC v. Epic Games, Inc. — United States

The Federal Trade Commission challenged practices associated with Fortnite's interface and purchasing system.

The FTC alleged that Epic's design facilitated unintended purchases, including through button configurations and other interface mechanisms.

Competition significance

Although principally a consumer-protection matter, the case demonstrates how interface architecture can influence commercial behaviour at massive digital-platform scale.

It provides an important analytical foundation for understanding how digital design can affect user choice.

3. FTC v. Match Group, Inc. — United States

The FTC brought proceedings concerning Match Group's online dating services and alleged deceptive practices involving subscriptions, cancellation, and user interfaces.

Competition significance

The case illustrates the relationship between:

• subscription design;
• consumer retention;
• cancellation friction;
• digital switching costs.

Where similar mechanisms are used by a dominant platform to disadvantage competitors, the same economic concepts can become relevant to competition-law analysis.

4. Google Shopping — European Union

The European Commission found Google liable for favouring its comparison-shopping service in search results.

The case principally concerned self-preferencing and search-result positioning, rather than dark patterns in the narrow consumer-protection sense.

Competition significance

It is highly relevant because it demonstrates how interface presentation and prominence can influence competitive visibility.

A platform controls the interface through which consumers access competing services. Manipulation of that interface can therefore have foreclosure effects.

The important principle is that competitive harm can arise from how a platform presents choices, not merely from the price or contractual terms of the underlying product.

5. Google Android — European Union

The European Commission's Android decision concerned practices involving Google's mobile ecosystem, including contractual restrictions associated with search and browser distribution.

Competition significance

Defaults, pre-installation and ecosystem design can influence user behaviour and reinforce network effects.

The case is therefore relevant to the broader competition analysis of dark patterns because:

interface defaults + ecosystem control + distribution advantages

can potentially strengthen an incumbent's position and reduce opportunities for competing services.

6. Google Android Auto / Enel X — European Union

The European Commission's investigation into Google's Android Auto concerned restrictions affecting compatibility between applications and Google's automotive platform.

Competition significance

This demonstrates another important dimension of digital-interface competition:

access to an ecosystem's interface can itself become a competitive bottleneck.

Where a dominant platform determines which functionality competitors can expose through the interface, interface governance may affect market access.

7. Apple App Store Practices — European Union

The European Commission has examined Apple's App Store rules and practices under EU competition law, including issues concerning steering and access to alternative purchasing arrangements.

Competition significance

Interface restrictions can affect whether developers are able to communicate alternative purchasing options to consumers.

This illustrates the distinction between:

• controlling an interface for legitimate technical reasons; and
• using interface control to restrict competitive alternatives.

The latter may raise competition concerns where dominance and foreclosure are established.

10. Competition-Law Theories Potentially Engaged

11. Dark Patterns and Article 102 TFEU

Under Article 102 TFEU, dark patterns could become relevant where they form part of conduct by a dominant undertaking.

Potential theories include:

Abuse through unfair conditions

Article 102(a) addresses unfair purchase or selling prices or other unfair trading conditions.

A dark pattern could become relevant where the interface facilitates the imposition of materially unfair conditions.

Limiting markets or technical development

Article 102(b) may become relevant where interface restrictions limit consumer choice, innovation, or development of competing services.

Discriminatory treatment

Article 102(c) may become relevant where interface access or presentation discriminates between comparable trading partners.

Leveraging

A dominant undertaking may potentially use control over one platform market to strengthen its position in an adjacent market.

12. Dark Patterns Under Section 19/Section 4 of the Indian Competition Act

In India, dark patterns can intersect with competition law particularly where a platform has a dominant position.

Potentially relevant issues under Section 4 of the Competition Act, 2002 include:

• unfair or discriminatory conditions;
• limiting consumer choice;
• limiting market access;
• leveraging dominance;
• exclusionary platform conduct.

The Competition Commission of India has increasingly examined digital ecosystems, platform conduct, self-preferencing, data advantages and intermediary power.

However, not every dark pattern constitutes an abuse of dominance.

The competition analysis must establish the necessary elements of the particular infringement.

13. Consumer Protection and Competition Law Overlap

Dark patterns sit at the intersection of two regulatory regimes.

Consumer-protection perspective

The principal question is:

Was the individual consumer deceived, manipulated or unfairly induced?

Competition perspective

The principal question is:

Did the conduct distort competitive conditions or reinforce market power?

The same interface can therefore produce two distinct legal inquiries.

For example:

Difficult cancellation

Consumer law:
→ Was the consumer unfairly prevented from cancelling?

Competition law:
→ Did the practice materially increase switching costs and protect a dominant platform from competitive pressure?

14. Economic Effects

Dark patterns may affect competition through several economic mechanisms.

1. Higher switching costs

Users remain with incumbent platforms.

2. Reduced multi-homing

Users are discouraged from simultaneously using rival services.

3. Increased network effects

The incumbent acquires more users and data.

4. Data accumulation

More user interactions generate additional behavioural data.

5. Reduced contestability

Competitors face greater difficulty acquiring customers.

6. Higher entry barriers

New entrants must overcome artificially reinforced incumbent advantages.

7. Reduced innovation

Competitors may have weaker incentives to develop alternative products.

15. Dark Patterns and Data Advantage

A particularly important issue for modern competition law is the relationship between dark patterns and data accumulation.

Consider:

Manipulative consent interface
↓
More users disclose data
↓
Larger behavioural dataset
↓
Better prediction/targeting
↓
Better monetisation
↓
Greater investment capacity
↓
Stronger competitive position

This creates a potential data-feedback loop.

The relevant competition question is therefore not simply whether the initial interface was deceptive, but whether the resulting data accumulation contributes to durable market power.

16. Dark Patterns and Entry Barriers

An incumbent may have several advantages unavailable to a new entrant:

• large existing user base;
• extensive behavioural data;
• established defaults;
• integrated payment infrastructure;
• pre-installed software;
• established identity systems;
• ecosystem interoperability.

Dark patterns may reinforce these advantages.

Thus:

Existing market power + interface control + switching friction

can create an increasingly difficult environment for new entrants.

17. Dark Patterns and Consumer Choice Architecture

Competition law traditionally assumes that consumers can compare competing offers.

Digital platforms can challenge this assumption because they control the choice architecture through which consumers encounter those offers.

A platform may determine:

1. which options appear;
2. where they appear;
3. how prominently they appear;
4. what the default option is;
5. how much friction each choice requires;
6. when alternatives are displayed.

Consequently, control over choice architecture can become economically significant market power.

18. Defences and Objective Justifications

A platform should not automatically be treated as engaging in anticompetitive conduct merely because its interface uses defaults or persuasive design.

Legitimate reasons may include:

• fraud prevention;
• cybersecurity;
• accessibility;
• regulatory compliance;
• payment security;
• reduction of accidental transactions;
• simplified user experience;
• technical compatibility;
• protection of minors.

Therefore, enforcement should distinguish between:

legitimate friction
and
strategically imposed competitive friction.

The latter becomes more significant where it is unnecessary, asymmetric, persistent, and capable of foreclosing rivals.

19. Key Legal Test

A structured competition-law investigation can examine:

Step 1 — Relevant market

Identify the relevant product/service and geographic market.

Step 2 — Market power

Determine whether the undertaking possesses substantial market power or dominance.

Step 3 — Interface conduct

Identify the precise dark pattern.

Step 4 — Counterfactual

Ask what the interface would look like absent the allegedly problematic design.

Step 5 — Competitive effect

Examine whether the design:

• raises switching costs;
• forecloses competitors;
• reduces multi-homing;
• disadvantages rivals;
• strengthens network effects.

Step 6 — Consumer effect

Examine:

• choice;
• price;
• quality;
• privacy;
• innovation;
• data exploitation.

Step 7 — Causation

Determine whether the dark pattern materially contributed to the competitive harm.

Step 8 — Justification

Consider legitimate technical, commercial or regulatory explanations.

20. Evidentiary Issues

Dark-pattern competition cases may require extensive digital evidence.

Important evidence includes:

• interface screenshots;
• A/B testing records;
• product-design documents;
• internal emails;
• UX research;
• algorithmic optimisation records;
• conversion-rate experiments;
• cancellation-rate data;
• switching statistics;
• customer complaints;
• source-code records;
• ranking algorithms;
• recommendation-system outputs.

Particularly important is evidence showing that the design was deliberately optimised to produce a particular behavioural or competitive outcome.

21. Regulatory Challenges

Dark-pattern cases create several enforcement difficulties.

A. Intent is difficult to establish

A harmful design may arise from ordinary optimisation rather than an explicit anticompetitive strategy.

B. Effects may be cumulative

No individual interface element may produce substantial foreclosure.

C. Algorithms constantly change

The interface tested by regulators may not be the interface used months later.

D. Personalisation complicates evidence

Different users may receive different interfaces.

E. Consumer and competition harms overlap

It may be difficult to separate deception from competitive foreclosure.

22. Emerging Issue: AI-Generated Dark Patterns

Generative AI could substantially expand dark-pattern practices.

An AI system could dynamically generate:

• personalised subscription prompts;
• cancellation obstacles;
• urgency messages;
• recommendations;
• pricing presentations;
• persuasive text;
• personalised defaults.

The system could continuously test which interface produces the greatest conversion or retention.

This creates a potential shift from:

static dark patterns

to

adaptive algorithmic manipulation.

Competition authorities may therefore need to examine not merely the interface but the objective function governing the interface.

23. Overall Legal Significance

Dark patterns do not constitute a standalone competition-law infringement merely because they are manipulative.

Their competition significance arises where they interact with:

• dominance;
• network effects;
• switching costs;
• data accumulation;
• self-preferencing;
• tying;
• ecosystem control;
• interoperability restrictions;
• foreclosure;
• exclusion of rivals.

The strongest competition-law cases are therefore likely to involve a demonstrable chain:

Market power → control over digital interface → manipulative or discriminatory design → increased switching/data/network advantages → foreclosure or exploitation → harm to competitive process.

Conclusion

Dark patterns represent an important development in digital competition law because market power increasingly operates through control over consumer choice architecture rather than merely through prices.

The central legal challenge is to distinguish ordinary persuasive interface design from conduct that exploits substantial market power to restrict competitive alternatives.

The most significant areas for competition-law analysis are switching costs, defaults, self-preferencing, data accumulation, tying, interoperability, ecosystem lock-in, algorithmic personalisation and platform foreclosure.

The developing jurisprudence involving major digital platforms—particularly Amazon, Google, Apple, Epic Games and Match Group—shows that interface design, subscription architecture and control over digital choice environments can have substantial legal significance, although the precise legal basis may arise under competition law, consumer protection law, or both.