Civil Law and UAE: Platform Liability in Algorithmic Systems

1. Introduction

Platform liability in algorithmic systems concerns the civil responsibility of a digital platform when an algorithm, automated decision-making tool, AI system, recommendation engine, matching system, ranking system, fraud-detection tool, pricing algorithm, or other automated process causes loss or legal harm.

Examples include:

• an e-commerce platform's algorithm incorrectly classifying a product;
• an AI system recommending a defective or unlawful product;
• an algorithm suspending a seller's account without contractual justification;
• an automated pricing system causing financial loss;
• an AI recommendation system facilitating fraud;
• an algorithmic credit or financial decision causing loss;
• an automated platform allowing misuse of personal data;
• a digital-asset platform incorrectly executing or controlling a transaction;
• an algorithm producing discriminatory or misleading results.

UAE law does not currently operate through one single general statute saying that a platform is automatically liable whenever its algorithm causes harm. Instead, liability may arise from contract, civil wrong, statutory duties, consumer protection, data protection, financial regulation, intellectual property, cybersecurity, and the particular functions undertaken by the platform.

The DIFC framework is especially significant because its Digital Economy Court expressly covers AI, digital assets, online intermediaries, digital payment platforms, marketplaces, databases, cloud systems, DLT/blockchain and related technology disputes.

The UAE's new Civil Transactions Law, Federal Decree-Law No. 25 of 2025, repealed the 1985 Civil Transactions Law and entered into force on 1 June 2026, so current mainland analysis should be made against the new law rather than treating the former Civil Code as still operative.

2. Meaning of Platform Liability

A platform is a digital environment that connects or facilitates transactions between different participants.

Examples:

• e-commerce marketplaces;
• food-delivery platforms;
• ride-hailing platforms;
• financial technology platforms;
• digital-payment platforms;
• cryptocurrency exchanges;
• social-media platforms;
• property platforms;
• AI-enabled marketplaces;
• cloud-based business platforms.

An algorithmic system is a computer-based process that uses programmed rules, statistical models, machine learning or AI to produce outputs or make recommendations or decisions.

Therefore:

Platform algorithmic liability = responsibility arising when the platform's automated system, its operation, its representations, or its failure to exercise an applicable duty causes legally recoverable loss.

3. Why Algorithmic Platform Liability Is Difficult

Traditional civil liability generally asks:

1. Who acted?
2. What obligation existed?
3. Was there a breach?
4. Was there fault?
5. Did the breach cause the damage?
6. What damage occurred?
7. What remedy should follow?

Algorithmic systems complicate these questions because several persons may participate:

Platform owner → Algorithm developer → Data provider → Cloud provider → Seller/service provider → Payment provider → Consumer

The algorithm itself is not normally treated as an independent legal person.

Thus, the central question is usually:

Which human or corporate legal person controlled, supplied, deployed, represented or benefited from the algorithmic system, and what legal obligation did that person assume?

4. Legal Foundations of Liability in the UAE

A. Contractual liability

A platform may be liable where:

• its terms promise a particular service;
• it undertakes to maintain the platform;
• it promises security;
• it undertakes to process transactions correctly;
• it guarantees particular functionality;
• it fails to perform agreed services;
• an algorithm performs contrary to contractual specifications.

For example, if a fintech platform promises to execute transactions according to specified instructions but its automated system improperly processes them, contractual liability may arise depending upon the agreement and applicable law.

B. Civil/tort liability

Algorithmic liability may also arise independently of contract.

Potential situations include:

• negligent deployment of an AI system;
• inadequate safeguards;
• foreseeable algorithmic error;
• failure to prevent known risks;
• negligent data processing;
• misleading representations;
• wrongful interference with another person's property or rights.

The claimant still generally needs to establish the legally relevant elements of the applicable civil wrong, including causation and damage.

C. Statutory liability

Special legislation may impose additional duties concerning:

• consumer protection;
• personal data;
• financial services;
• electronic transactions;
• intellectual property;
• cybersecurity;
• digital assets;
• employment;
• regulated technology.

Therefore, a platform cannot necessarily avoid liability merely by describing itself as a "technology provider."

5. The Functional Approach to Platform Liability

One of the most important principles is to examine what the platform actually does.

Consider two platforms.

Platform A

It merely provides technical hosting.

Platform B

It:

• selects sellers;
• verifies sellers;
• controls payments;
• controls customer communications;
• determines rankings;
• recommends products;
• controls refunds;
• stores customer information;
• operates the algorithm;
• makes representations concerning safety.

The second platform has undertaken substantially more functions.

Consequently, the legal analysis can differ substantially.

Practical formula

Greater Function + Greater Control + Greater Representation = Potentially Greater Responsibility

This is not an automatic statutory rule; it is a useful framework for analysing the underlying contractual and civil obligations.

6. Algorithmic Control

Courts may need to consider who controls:

• algorithm design;
• training data;
• model deployment;
• system updates;
• recommendation rules;
• automated decisions;
• access permissions;
• transaction execution;
• data storage;
• cybersecurity;
• human override mechanisms.

Control is particularly relevant where the platform itself designed or deployed the system.

7. Algorithmic Error and Negligence

Suppose an AI platform repeatedly recommends a fraudulent investment opportunity.

Potential questions include:

1. Was the risk reasonably foreseeable?
2. Did the platform know about previous complaints?
3. Did it have monitoring systems?
4. Did it have a human review mechanism?
5. Did it make representations about the reliability of the recommendation?
6. Was the recommendation generated automatically?
7. Did the user rely upon the recommendation?
8. Did the platform receive compensation for the transaction?
9. Did the platform have contractual or regulatory duties?

The mere fact that an algorithm made an error should not automatically establish civil liability.

The claimant must connect the algorithmic conduct to a legally recognized obligation and actionable damage.

8. Algorithmic Transparency

Transparency becomes important where a platform makes consequential automated decisions.

Examples:

• account termination;
• credit decisions;
• insurance pricing;
• investment recommendations;
• seller ranking;
• transaction blocking;
• fraud detection;
• content moderation.

A platform may face greater legal difficulty defending an automated decision if it cannot demonstrate:

• what the system was intended to do;
• what information it used;
• what contractual rule authorized the decision;
• whether the system malfunctioned;
• whether a human review mechanism existed;
• whether the decision was properly communicated.

This does not mean that UAE law presently establishes a universal rule requiring every commercial algorithm to be fully explainable.

9. Human Oversight

An important distinction is between:

Fully automated operation

The system makes the decision with little or no human intervention.

Human-assisted automation

The system generates a recommendation, but a human makes the final decision.

Automated execution with human supervision

The system executes transactions automatically but remains subject to human controls.

The more consequential the decision, the more important questions of monitoring, verification, auditability and human intervention may become.

10. Data as a Source of Algorithmic Liability

Algorithms frequently depend upon personal or commercially sensitive data.

Consequently, platform liability can overlap with data protection.

Potential problems include:

• collecting excessive information;
• inaccurate information;
• unauthorized processing;
• inadequate security;
• improper profiling;
• unlawful disclosure;
• misuse of customer information;
• algorithmic decisions based on inaccurate data.

This creates a dual liability problem:

Algorithmic error + unlawful data processing

A platform may therefore face different legal questions under different legal regimes arising from the same technological event.

11. Consumer Protection

Consumer-facing platforms require particular attention.

A platform may represent itself as merely an intermediary, but its actual functions and statutory obligations matter.

The UAE consumer-protection framework includes rules dealing with e-commerce providers and responsibilities connected with goods supplied through electronic platforms.

This is important because platform liability cannot always be determined simply by the platform's contractual label.

12. Algorithmic Misrepresentation

Suppose a platform's algorithm automatically displays:

"Verified Seller"

but the seller was never adequately verified.

The consumer might argue that the platform created a misleading representation.

Similarly:

"Lowest Price Guaranteed"

"100% Safe"

"AI-verified"

"Fraud-free"

could potentially become legally relevant representations depending upon their meaning, context and applicable statutory or contractual duties.

The key question is not merely whether the statement was produced by an algorithm.

The question is:

Who caused the representation to be made and what legal responsibility accompanied it?

13. Platform Liability for Recommendation Algorithms

Recommendation systems can create disputes involving:

• defective products;
• unsafe services;
• fraudulent sellers;
• misleading financial products;
• illegal content;
• unsuitable investments.

However, recommendation alone does not automatically make the platform responsible for everything recommended.

Courts may need to examine:

• whether the recommendation was paid or sponsored;
• whether the platform selected the seller;
• whether the platform verified the seller;
• whether the platform represented that the seller was trustworthy;
• whether the platform knew about the risk;
• whether the user relied on the recommendation;
• contractual arrangements between platform and seller.

14. Platform Liability for Automated Financial Systems

Financial and digital-asset platforms create particularly complex liability issues.

Possible disputes involve:

• automated transfers;
• cryptocurrency wallets;
• algorithmic trading;
• payment processing;
• custody;
• transaction verification;
• fraud detection;
• automated account restrictions.

The DIFC Digital Economy Court's jurisdiction expressly includes digital assets, digital payment platforms, virtual-asset service providers and AI-related disputes.

15. Case Law

Because reported UAE cases specifically deciding "algorithmic platform liability" as a standalone doctrine remain limited, the following authorities are best understood as closely relevant technology, platform, digital-asset, contractual, regulatory and procedural authorities.

Case 1 — Gate Mena DMCC v Tabarak Investment Capital Ltd [2024] DIFC DEC 002

This is one of the most important technology-platform authorities.

The dispute concerned a 300 BTC transaction involving an intermediary, cryptocurrency wallets and custody arrangements.

The Digital Economy Court examined the actual role performed by Tabarak rather than simply relying upon labels. The Court concluded that Tabarak's contractual obligation was to exercise reasonable care in maintaining control over the BTC, rather than accepting the broader strict-liability obligation argued by the claimants.

Relevance to algorithmic platforms

The case demonstrates that:

Liability depends significantly upon the actual function and contractual responsibility undertaken by the intermediary.

For algorithmic systems, this means a court may ask whether the platform:

• merely provided technology;
• controlled the transaction;
• guaranteed an outcome;
• undertook reasonable care;
• exercised custody;
• gave directions;
• assumed a particular risk.

Case 2 — Gate Mena DMCC v Tabarak Investment Capital Ltd [2023] DIFC CA 002

The Court of Appeal examined the earlier proceedings involving the cryptocurrency transaction and ordered a retrial concerning an issue arising from the parties' contractual relationship.

The subsequent 2024 Digital Economy Court judgment therefore provides an important example of how contractual obligations surrounding digital-asset platforms are determined.

Principle

A technology intermediary does not automatically become a guarantor of every technological or transactional risk.

The precise obligations must be identified from:

• contract;
• conduct;
• function;
• circumstances;
• allocation of risk.

16. Case 3 — Linux v Lizeth [2022] DIFC SCT 237

This case concerned a Software Development Agreement and an associated NDA.

The dispute arose from alleged contractual breaches relating to software development. The DIFC Small Claims Tribunal ultimately dismissed the claim.

Relevance

Although it was not an AI-liability case, it demonstrates the importance of the contractual architecture surrounding digital platforms.

For an algorithmic system, the contract may determine:

• who develops the software;
• who supplies the data;
• who tests the system;
• who maintains it;
• who bears cybersecurity responsibility;
• what performance standards apply;
• what happens when the software fails.

Thus:

Software architecture does not replace contractual responsibility.

17. Case 4 — Gavin v Gaynor [2015] DIFC CFI 017

The dispute involved a Stored Value Card Processing, Service and Marketing Agreement concerning services in the UAE. The defendant was affiliated with a technology-platform company developing and operating technology platforms internationally. The case involved contractual and jurisdictional questions and an arbitration agreement.

Relevance

The case illustrates that technology platforms often operate through interconnected corporate entities.

Accordingly, an algorithmic-platform dispute may require examination of:

• platform operator;
• technology company;
• parent company;
• service provider;
• contracting entity;
• arbitration agreement.

A claimant cannot necessarily treat every company in a technology group as one legal person.

18. Case 5 — Techteryx Ltd v Aria Commodities DMCC & Others [2025] DIFC DEC 001

This case involved a major digital-asset dispute and multiple defendants, including banks and commercial entities.

The DIFC Digital Economy Court granted proprietary and freezing relief concerning approximately USD 456 million and dealt with issues involving digital assets, tracing and the roles of multiple participants.

The case subsequently generated further Digital Economy Court orders involving additional parties and enforcement issues.

Relevance to algorithmic platforms

The case demonstrates that digital disputes can involve an ecosystem rather than one defendant.

For an algorithmic platform, potentially relevant actors may include:

• platform operator;
• algorithm developer;
• asset custodian;
• bank;
• payment processor;
• data provider;
• individual controller;
• related corporate entity.

Therefore:

Digital causation may require tracing the complete technological and contractual chain.

19. Case 6 — Oheo Bank v Parker [2025] DIFC CA 006

The DIFC Court of Appeal considered a dispute involving an arbitral award and duties concerning financial communications.

The underlying claim included an allegation that information supplied by the bank was misleading because it was incomplete. The Court of Appeal considered the legal basis for that finding and the threshold for court intervention in an arbitral award.

Relevance to algorithmic platforms

This is particularly useful for automated communications.

If an algorithm generates:

• investment explanations;
• risk warnings;
• product descriptions;
• financial information;
• automated notifications,

the fact that a computer generated the communication does not necessarily remove the legal significance of the communication.

The central question remains:

What duty applied to the entity responsible for the communication?

20. Case 7 — Anastasiia Denisova v Aleksei Galtcev & Realiste Holding Ltd [2024] DIFC CFI 041

This dispute involved an AI-technology platform facilitating real-estate investments and issues concerning the corporate and shareholder relationship surrounding the platform.

Relevance

The case is useful because it demonstrates that an AI platform can generate disputes extending beyond the algorithm itself.

Potential legal issues include:

• ownership;
• shareholders;
• intellectual property;
• employment;
• corporate control;
• platform development;
• contractual rights.

Thus, the existence of AI does not create a completely separate legal universe.

Traditional civil and corporate principles continue to operate around the technology.

21. Case 8 — Lural v Listran & Lokhan [2021] DIFC CA 003

This authority is relevant to the interaction between different UAE legal jurisdictions and questions of DIFC jurisdiction.

Relevance to platform disputes

Digital platforms frequently operate across:

• mainland UAE;
• DIFC;
• ADGM;
• foreign jurisdictions.

Consequently, before determining algorithmic liability, a court may need to determine:

1. Which court has jurisdiction?
2. Which law governs?
3. What contractual jurisdiction clause applies?
4. Where did the damage occur?
5. Where is the platform entity incorporated?
6. Where is the data located?
7. Where is the relevant asset located?

22. Important Principle From the Cases

The cases collectively support a cautious proposition:

A platform is not automatically liable merely because an algorithm caused an undesirable result. Liability depends upon the platform's legal obligation, actual function, control, representations, contractual undertakings, applicable legislation, causation and damage.

The Gate Mena litigation is particularly illustrative because the Court examined the specific function undertaken by the intermediary and rejected an attempt to convert that role into strict liability without an adequate contractual basis.

23. Algorithmic Platform Liability Matrix

24. Causation in Algorithmic Liability

Causation is often the most difficult issue.

Consider:

Bad data → Algorithmic error → Platform recommendation → Consumer reliance → Transaction → Financial loss

The court must determine where legally relevant causation exists.

The claimant may need to show:

Algorithmic conduct → legally relevant breach → reliance/causal connection → actual damage

The existence of an algorithmic error alone is insufficient to establish every element of civil liability.

25. Multiple Causes

An algorithmic loss may have several causes.

For example:

1. Seller supplied false information.
2. Platform algorithm relied upon that information.
3. Platform failed to verify it.
4. Consumer relied upon the recommendation.
5. Consumer suffered loss.

The court may therefore need to allocate responsibility among several actors.

This is particularly important for platform ecosystems because technical causation and legal causation are not necessarily identical.

26. Algorithmic Bias

Algorithmic bias can arise from:

• biased training data;
• incomplete datasets;
• flawed programming;
• discriminatory proxies;
• historical data;
• inappropriate classification.

Possible civil consequences depend on the applicable legal duty.

A court should not automatically convert the existence of statistical disparity into civil liability.

The claimant would need to connect the alleged bias to a legally actionable obligation and recoverable harm.

27. Explainability

Explainability becomes particularly important where the algorithm makes a decision affecting significant legal or economic interests.

A platform should ideally maintain:

• audit logs;
• version histories;
• input records;
• model documentation;
• decision records;
• human-review records;
• error reports;
• system alerts.

These records can help establish:

• what happened;
• who controlled the system;
• whether the system functioned as intended;
• whether reasonable safeguards existed;
• whether the loss was foreseeable.

28. Evidence in Algorithmic Liability

Algorithmic disputes create special evidentiary problems.

Relevant evidence may include:

• source code;
• model documentation;
• API logs;
• transaction logs;
• database records;
• timestamps;
• audit trails;
• training-data records;
• system prompts;
• model outputs;
• human override records;
• cybersecurity reports.

The challenge is often not merely whether an algorithm made a decision, but whether the claimant can establish a legally sufficient connection between the algorithmic event and the damage.

29. Contractual Allocation of Algorithmic Risk

Sophisticated platform contracts should specify:

Developer responsibility

Who develops and maintains the algorithm?

Data responsibility

Who supplies and verifies data?

Security responsibility

Who protects the system?

Accuracy

What level of accuracy is promised?

Human review

When must human intervention occur?

Downtime

Who bears losses caused by system failure?

AI limitation

Is AI output merely advisory?

Indemnity

Who indemnifies whom?

Liability cap

Is liability contractually limited?

Audit

Who can inspect algorithmic records?

Incident response

What happens after an algorithmic failure?

These contractual provisions can become central to litigation.

30. Platform Liability and Corporate Personality

A platform may consist of several companies:

Parent company → Technology subsidiary → UAE operating company → Payment entity → AI provider

A claimant cannot automatically treat them as one entity.

Separate corporate personality remains important.

Therefore, a claimant must identify the legal basis for imposing liability on each particular entity.

Possible bases include:

• contractual liability;
• guarantee;
• direct negligence;
• statutory liability;
• agency;
• personal wrongdoing;
• corporate obligations.

31. Strict Liability vs Reasonable Care

An important distinction is:

Strict liability

The defendant is responsible regardless of fault once specified conditions are established.

Reasonable-care obligation

The defendant is responsible for failing to exercise the level of care required by the applicable legal relationship.

The Gate Mena judgment is particularly useful here: the Court found that the intermediary was required to exercise reasonable care in maintaining control over the BTC, rather than bearing an unlimited strict obligation for loss occurring without fault.

This provides a useful analogy for algorithmic systems.

A platform should not automatically be treated as an insurer against every AI error unless the applicable law or contract creates such an obligation.

32. Remedies

Depending upon the applicable cause of action, potential remedies may include:

• compensation;
• restitution;
• specific performance;
• injunction;
• correction of records;
• cessation of unlawful processing;
• preservation of digital evidence;
• freezing orders;
• proprietary remedies;
• disclosure;
• contractual remedies.

Digital-asset litigation before the DIFC Digital Economy Court demonstrates that modern civil remedies can include powerful asset-preservation and tracing measures.

33. DIFC Digital Economy Court

The DIFC framework is particularly significant for this subject.

Part 58 expressly covers:

• artificial intelligence;
• digital assets;
• blockchain;
• databases;
• cloud data;
• e-commerce;
• online intermediaries;
• digital payment platforms;
• marketplaces;
• automatic dispute resolution;
• DAOs;
• DeFi;
• DApps;
• digital signatures;
• software;
• cybersecurity-related digital systems. 

The rules also contemplate electronic dynamic systems and AI-driven forms for certain court processes.

This demonstrates that UAE judicial infrastructure is adapting to technology-specific disputes.

34. Key Problems in Algorithmic Platform Liability

1. Black-box problem

The claimant may not know how the algorithm reached its result.

2. Responsibility gap

Several companies may participate in the system.

3. Causation problem

There may be several causes of the damage.

4. Data problem

Incorrect input may produce an apparently correct algorithmic output.

5. Contract problem

Platform terms may attempt to limit liability.

6. Jurisdiction problem

Different parts of the platform may operate in different jurisdictions.

7. Evidence problem

Important evidence may exist only in technical logs.

8. Human oversight problem

It may be unclear whether anyone reviewed the automated decision.

35. Practical UAE Legal Test

For an algorithmic-platform dispute, the following sequence is useful:

Step 1 — Identify the platform

Who legally owns and operates it?

Step 2 — Identify the algorithm

What exactly did it do?

Step 3 — Identify the function

Was it:

• recommending;
• ranking;
• deciding;
• executing;
• verifying;
• predicting;
• pricing;
• blocking;
• monitoring?

Step 4 — Identify the legal duty

Was the duty based on:

• contract;
• civil law;
• consumer law;
• data law;
• financial regulation;
• another statute?

Step 5 — Identify control

Who controlled the algorithm?

Step 6 — Identify reliance

Did someone rely upon the algorithmic output?

Step 7 — Establish causation

Did the relevant breach cause the damage?

Step 8 — Establish damage

What actual loss occurred?

Step 9 — Examine defences

Consider:

• contractual exclusions;
• contributory conduct;
• third-party fault;
• lack of causation;
• force majeure;
• limitation clauses;
• regulatory compliance.

Step 10 — Select remedy

Determine the appropriate compensation, injunction, restitution, disclosure or other remedy.

36. Six Core Principles for Examination

1. Algorithm is not a legal person — responsibility normally attaches to the relevant human or corporate actor.
2. Platform status alone does not establish automatic liability.
3. Actual platform function matters — intermediary, custodian, operator, recommender, payment provider or service provider may have different obligations.
4. Contractual allocation of risk is important.
5. Causation and actual damage must be established.
6. Special UAE legislation may supplement general civil liability, particularly in consumer, data, financial and digital-asset contexts.

37. Case-Law Revision Table

38. Conclusion

Platform liability for algorithmic systems in UAE civil law is developing through the interaction of traditional civil-law principles and technology-specific regulation and jurisprudence.

The central principle is:

The use of an algorithm does not by itself create liability and does not by itself eliminate liability.

The court must examine:

Platform + Algorithm + Function + Contract + Control + Representation + Applicable Statutory Duty + Causation + Damage + Remedy

The developing DIFC Digital Economy Court framework is particularly significant because it expressly brings AI, digital platforms, online intermediaries, digital payments, marketplaces, blockchain and digital assets within a specialist judicial framework.

At the same time, the reported cases show that courts are generally likely to examine the actual legal relationship and functions performed by the platform, rather than imposing unlimited liability simply because technology was involved. The Gate Mena decision is a particularly clear illustration of this functional approach.

Quick Revision Formula

Algorithmic Platform Liability =

Legal Duty + Platform Function + Control + Algorithmic Conduct + Causation + Actual Damage + Appropriate Remedy

One-line principle:

In UAE civil law, algorithmic operation changes the factual mechanism of harm, but liability still depends on an identifiable legal obligation and a legally sufficient connection between the platform's conduct and the claimant's damage.