Civil Law and UAE Algorithmic Amplification of Harm Responsibility

1. Introduction

Algorithmic amplification of harm refers to situations where an automated system, recommendation engine, ranking algorithm, advertising system, social-media platform, AI model, or data-driven decision system does more than merely host information: it increases the visibility, reach, speed, persistence, or impact of harmful content or conduct.

Examples include:

an algorithm repeatedly recommending defamatory content;

an AI system amplifying misinformation about a person or business;

a platform algorithm promoting fraudulent investment material;

an automated advertising system directing harmful content toward vulnerable users;

an algorithm repeatedly ranking unsafe or misleading information highly;

automated systems causing discriminatory or reputational harm through profiling;

recommendation systems increasing the economic loss caused by fraudulent schemes.

In UAE civil law, the central question is not simply “Who created the harmful content?” but increasingly:

Who had sufficient control over the algorithmic process that amplified the harm, and did that person or entity act negligently, unlawfully, or abusively?

The issue is particularly important under the UAE's current civil-law framework following the entry into force of the Federal Decree-Law No. 25 of 2025 promulgating the Civil Transactions Law on 1 June 2026.

2. Meaning of Algorithmic Amplification

There is an important distinction between creation of harm and amplification of harm.

Traditional model

A person creates harmful content:

Wrongful act → Victim → Damage

Algorithmic model

The structure may be:

Creation → Algorithmic selection → Ranking → Recommendation → Repetition → Wider audience → Increased harm

For example, a defamatory post may initially reach 100 people. An algorithm may identify high engagement and recommend it to 100,000 additional users.

The algorithm has therefore potentially contributed to the scale of the damage, even though the original statement was made by another person.

3. UAE Civil-Law Foundation

Algorithmic amplification responsibility can be analysed through several fundamental civil-law principles.

A. Unlawful harm

The traditional UAE civil-liability framework recognizes responsibility where unlawful conduct causes legally recognized damage.

The foundational provision under the former Civil Transactions Law was Article 282, which established the general principle of liability for causing harm.

Although cases decided under the former 1985 Civil Transactions Law remain important, they should now be treated as historical and doctrinal authorities when applying the new Civil Transactions Law effective from 1 June 2026.

B. Abuse of rights

The UAE civil-law system also recognizes that even the exercise of an apparently lawful right may become unlawful where it constitutes an abuse of rights.

This principle is particularly relevant to digital platforms.

A platform may have a legitimate right to:

rank content;

recommend content;

personalize feeds;

operate advertising systems;

moderate users;

monetize engagement.

But the exercise of those powers may create liability if they are exercised in a manner that unjustifiably causes serious harm.

The current Civil Transactions Law retains the abuse-of-rights principle in Article 106, corresponding substantially to the former Article 106 framework.

4. Algorithmic Amplification Is Not Automatically Negligence

An important principle is:

Algorithmic amplification alone does not automatically establish civil liability.

The claimant normally needs to establish the relevant elements of liability.

These may include:

unlawful conduct or breach of a legal duty;

actual damage;

causal connection;

attribution to the defendant;

absence of an applicable defence;

where relevant, fault, negligence, or abuse.

Therefore, simply showing that an algorithm recommended harmful material may not be sufficient.

The claimant may need to demonstrate that the defendant:

knew or should reasonably have known of the risk;

designed the system in an unreasonably dangerous manner;

ignored repeated complaints;

failed to implement reasonable safeguards;

deliberately optimized harmful engagement;

materially increased the foreseeable damage.

5. Attribution Problem

One of the most difficult issues is attribution.

Suppose:

Person A creates defamatory content.

Platform B's algorithm recommends it.

Person C shares it.

Platform B's recommendation causes 1 million additional views.

Who is responsible?

There may potentially be multiple causal actors.

The court must distinguish:

Primary wrongdoer

The person who originally created the unlawful material.

Amplifier

The platform, algorithm operator, advertiser, intermediary, or other actor whose system substantially increased exposure.

Secondary participants

Persons who subsequently repost, distribute, modify, or otherwise contribute to the harm.

This creates a multi-actor civil-liability problem.

6. Causation in Algorithmic Amplification

Causation becomes considerably more complicated when algorithms are involved.

A claimant may need to establish:

Algorithmic intervention → Increased exposure → Increased harmful conduct → Additional damage

For example:

A fraudulent investment advertisement causes AED 10,000 of loss to an individual. If the platform's recommendation algorithm repeatedly targets the advertisement at thousands of users, the algorithmic system may become relevant to the causal analysis.

The court may ask:

Was the harm foreseeable?

Did the algorithm materially increase exposure?

Was the amplification substantial?

Was the harm caused by the original content or by the amplification?

Did another independent event break the causal chain?

Did the victim contribute to the loss?

7. Foreseeability

Foreseeability is particularly important.

An algorithmic operator may be in a stronger position than an ordinary individual to understand:

engagement patterns;

recommendation effects;

user vulnerability;

repeated exposure;

virality;

automated targeting;

manipulation risks.

Consequently, where an operator has sophisticated knowledge about how its system behaves, the argument for foreseeability may become stronger.

For example, if internal testing repeatedly shows that a recommendation system promotes fraudulent content because it generates high engagement, continuing to deploy the system without reasonable safeguards may strengthen a civil-liability claim.

8. Knowledge and Notice

Responsibility may also increase where the defendant has actual or constructive knowledge.

Relevant evidence could include:

user complaints;

regulatory warnings;

internal risk assessments;

algorithmic audit reports;

previous incidents;

content-removal requests;

safety testing;

internal communications;

system logs;

risk-management documents.

The more information an operator possesses about a harmful algorithmic pattern, the harder it may become to argue that the amplification was entirely unforeseeable.

9. Algorithmic Design as a Source of Liability

A major development is the possibility of analysing the design of the algorithm itself.

Potentially relevant design decisions include:

maximizing engagement at all costs;

recommending increasingly extreme content;

prioritizing sensational content;

insufficient fraud detection;

inadequate identity verification;

repeated recommendation of known harmful material;

failure to incorporate safety constraints.

The civil-law question becomes:

Was the algorithm reasonably designed and operated given the foreseeable risks?

This resembles traditional product and professional-liability reasoning, but applied to digital systems.

10. Algorithmic Amplification and Abuse of Rights

Article 106's abuse-of-rights principle can be particularly important.

A digital operator may have a contractual or commercial right to:

operate a platform;

personalize content;

maximize advertising revenue;

recommend material;

monetize user attention.

However, exercising those rights may become abusive if the operator:

intentionally exploits a known vulnerability;

deliberately promotes harmful material for profit;

disproportionately interferes with another person's rights;

causes serious harm without legitimate justification.

Thus:

Lawful technological function ≠ unlimited legal immunity.

11. Algorithmic Amplification of Defamation

Suppose a defamatory statement is posted by User A.

The platform algorithm then:

identifies high engagement;

recommends the statement;

pushes it to additional users;

repeatedly displays it;

sends notifications;

places it in trending results.

The original publisher and the platform may occupy different legal positions.

The platform's liability would depend upon applicable legislation, its knowledge, control, contractual arrangements, conduct, and causal contribution.

The central question would be:

Did the platform merely provide a neutral technological environment, or did its own conduct materially contribute to the harmful dissemination?

12. Algorithmic Amplification of Fraud

Fraud provides another important example.

Imagine an AI recommendation system identifies fraudulent investment advertisements as highly engaging and repeatedly recommends them to users.

Possible actors include:

fraudster;

advertiser;

platform;

payment intermediary;

algorithm provider;

data provider.

The civil court may need to determine the responsibility of each actor separately.

Particularly important questions include:

Who knew about the fraud?

Who controlled the algorithm?

Who received financial benefits?

Who could have stopped the amplification?

Was the fraud foreseeable?

Did the platform receive complaints?

Did the algorithm specifically target vulnerable consumers?

13. Algorithmic Amplification and Privacy

Algorithms may amplify harm through personal-data processing.

For example:

An algorithm uses personal information to identify individuals as financially vulnerable and repeatedly targets them with high-risk financial advertisements.

Potential harm may involve:

privacy;

economic loss;

discrimination;

reputational damage;

manipulation.

Civil responsibility may therefore overlap with:

privacy law;

data protection;

cybersecurity;

consumer protection;

contractual liability;

tort/civil liability.

14. Algorithmic Discrimination

Algorithmic amplification can also produce unequal treatment.

For example, an automated system may repeatedly amplify:

employment advertisements to one demographic;

housing advertisements to one category of users;

financial advertisements to particular groups;

political or commercial messages based on sensitive characteristics.

The legal issue becomes whether the algorithmic process produced legally actionable discriminatory harm.

The claimant may need to demonstrate:

algorithmic criterion → unequal treatment → legally recognized damage

15. AI and the New Civil Transactions Law

The new UAE Civil Transactions Law creates a broader modern framework while retaining fundamental civil-law concepts.

For technological disputes, important principles include:

good faith;

prohibition of abuse of rights;

compensation for unlawful harm;

causation;

contractual responsibility;

interpretation according to applicable legal principles.

The new Code should therefore not be understood as requiring a separate civil-liability regime for every new technology.

Instead, existing principles can potentially be adapted to new technological facts.

16. Contractual Responsibility

Algorithmic amplification may also generate contractual liability.

Suppose a platform promises:

safe advertising;

content moderation;

fraud prevention;

accurate recommendation;

data security.

If it fails to perform those obligations, contractual liability may arise depending on:

contract wording;

applicable law;

good faith;

limitation clauses;

causation;

damage.

The UAE principle of good-faith contractual performance is particularly relevant.

Under the current Civil Transactions Law, Article 121 preserves the fundamental good-faith approach to contractual performance.

17. Good Faith and Algorithmic Systems

Good faith may require parties to avoid conduct that defeats legitimate contractual expectations.

For example, a platform may contractually promise reasonable fraud controls but secretly configure its algorithm to prioritize fraudulent advertisements because they produce higher revenue.

Such conduct could raise questions concerning:

contractual good faith;

abuse of contractual rights;

causation;

compensation.

18. Multiple Tortfeasors

Algorithmic amplification frequently involves multiple actors.

For example:

The court must determine whether responsibility lies with:

one actor;

several actors jointly;

actors in proportion to their causal contribution;

or different actors for different categories of damage.

19. Evidence in Algorithmic-Amplification Cases

The UAE Evidence Law is particularly important.

Federal Decree-Law No. 35 of 2022 recognizes electronic evidence and covers matters such as:

electronic records;

electronic instruments;

electronic signatures;

electronic correspondence;

modern communication methods;

electronic media;

other electronic evidence.

This can allow parties to rely upon:

algorithm logs;

recommendation records;

timestamps;

system-generated reports;

emails;

platform messages;

moderation records;

audit trails;

technical expert reports.

The difficult issue is not simply whether the evidence is electronic, but whether it establishes:

what the algorithm actually did and whether that conduct caused the claimed harm.

20. AI-Generated Evidence

AI-generated evidence requires additional caution.

A party may produce:

AI-generated screenshots;

synthetic audio;

deepfake video;

AI-generated correspondence;

AI-generated reports.

The court must consider:

authenticity;

integrity;

provenance;

attribution;

reliability;

chain of custody;

relevance;

corroboration.

AI output should not automatically be treated as conclusive evidence.

21. Case Law

Because direct UAE reported case law specifically addressing algorithmic amplification is still limited, the following authorities are best understood as foundational or analogical authorities. They establish principles of abuse of rights, causation, contractual good faith, evidence, and judicial assessment that can be applied to algorithmic-amplification disputes.

Case 1 — Abu Dhabi Court of Cassation, Case No. 55/2016

Principle

The Abu Dhabi Court of Cassation considered the doctrine of abuse of rights under the former Article 106 of the Civil Transactions Law.

The principle recognizes that a person cannot necessarily escape civil consequences merely because an act initially appears to fall within an apparently lawful right.

Relevance

For algorithmic amplification, a platform's right to:

recommend;

rank;

advertise;

personalize;

cannot necessarily be treated as unlimited.

Where technological powers are exercised abusively and produce unjustified harm, Article 106 principles may become relevant.

Relevance: Directly foundational, not an AI-specific precedent.

22. Case 2 — Dubai Court of Cassation, Civil Appeal No. 6/2017

Principle

This authority concerns contractual obligations, acceptance of an employment offer, and the importance of contractual commitment and good-faith principles.

Relevance

Algorithmic systems frequently operate under contractual frameworks.

For example:

platform-user agreements;

AI-service agreements;

advertising contracts;

SaaS contracts;

algorithm-development agreements.

Where a party makes a contractual commitment concerning safety, moderation, performance, or data processing, the court may examine whether the commitment was performed consistently with contractual obligations.

Relevance: Contractual and good-faith analogy.

23. Case 3 — Abu Dhabi Court of Cassation, Case No. 922/2020

Principle

The case concerns arbitration authority and contractual good-faith principles.

Relevance to Algorithms

Algorithmic disputes may increasingly enter arbitration through:

technology contracts;

SaaS agreements;

platform agreements;

AI-development contracts;

data-processing contracts.

The authority is useful for the proposition that contractual arrangements and agreed dispute-resolution mechanisms should be interpreted within the broader framework of good faith and legal authority.

Relevance: Foundational contractual analogy.

24. Case 4 — Dubai Court of Cassation, Appeal No. 313/2007

Principle

The case addressed contractual termination and the exercise of contractual powers.

The important principle is that contractual powers are not necessarily exercised in a legal vacuum; their exercise remains subject to applicable legal limitations and contractual standards.

Relevance

A platform may possess contractual powers to:

suspend accounts;

remove material;

alter algorithms;

change ranking;

terminate services.

The exercise of these powers can potentially be challenged where it violates contractual obligations or constitutes legally impermissible conduct.

Relevance: Contractual-power analogy.

25. Case 5 — Dubai Court of Cassation, Appeal No. 440/2016

Principle

The case concerns contractual termination, contractual stability and good-faith considerations.

Relevance

Algorithmic systems frequently modify contractual relationships automatically.

For example:

An AI-driven platform may automatically suspend a merchant based upon a risk score.

If the automated decision is contractually significant, the court may have to determine whether the system's operation was consistent with:

contractual terms;

good faith;

procedural requirements;

evidence;

legitimate expectations.

Relevance: Foundational contractual analogy.

26. Case 6 — Dubai Court of Cassation, Case No. 266/2008

Principle

The case involved construction delay and the assessment of concurrent causes.

The important lesson is that damage may arise from multiple contributing causes, requiring analysis of each cause and the responsibility attributable to the parties.

Relevance to Algorithmic Amplification

This is highly useful conceptually.

Suppose damage results from:

Fraudulent content + algorithmic recommendation + reposting + victim's conduct.

The court may need to analyse each causal factor rather than assume that one actor exclusively caused the loss.

Relevance: Strong causation analogy.

27. Case 7 — Dubai Court of Cassation, Case No. 1/2006

Principle

This authority also concerns concurrent delay and allocation of responsibility among multiple contributing causes.

Relevance

Algorithmic amplification frequently involves causal fragmentation.

For example:

The original creator caused the initial injury, while the recommendation algorithm multiplied its reach.

The court may therefore need to distinguish:

initial harm;

incremental harm;

independent intervening causes;

foreseeable amplification;

victim contribution.

Relevance: Causation and apportionment analogy.

28. Case 8 — UAE Federal Supreme Court, Civil Appeal No. 79/2020

Principle

The Federal Supreme Court addressed the legal significance of admission and the conditions under which a person's acknowledgment can establish a right.

Relevance to AI Systems

An AI-generated statement should not automatically be treated as a human admission.

The claimant must establish:

who generated it;

whose account/system produced it;

whether a person authorized it;

whether it represents the defendant's position;

whether the system was manipulated.

Thus:

AI output ≠ automatically human admission.

This distinction is particularly important where an AI chatbot allegedly makes a representation causing financial or reputational harm.

Relevance: Evidentiary attribution analogy.

29. Case 9 — UAE Federal Supreme Court, Commercial Appeal No. 215/2020

Principle

The Federal Supreme Court emphasized the importance of properly reasoned expert evidence and held that a judgment should not merely rely mechanically on an expert report without explaining the basis for adopting its conclusions.

Relevance

This principle is extremely important for algorithmic disputes.

A party may present an:

AI audit;

algorithmic-risk report;

machine-learning analysis;

technical expert report.

The court should not simply say:

“The algorithm said X, therefore X is proven.”

Instead, the court must consider the technical evidence and independently assess its legal significance.

Relevance: Strong analogy for AI/algorithmic evidence.

30. Case 10 — UAE Federal Supreme Court, Penal Cassation No. 1422/2022

Principle

The Court emphasized that evidence supporting a judicial conclusion must possess sufficient probative force and that the court should demonstrate that it examined and scrutinized the evidence.

Relevance

This is particularly important where algorithmic evidence is presented.

A recommendation score, risk score, probability estimate, or AI-generated conclusion should not automatically become the factual basis of liability.

The court should examine:

source;

methodology;

reliability;

context;

competing evidence.

Relevance: Evidentiary analogy.

31. Case 11 — UAE Federal Supreme Court, Penal Cassation No. 660/2023

Principle

The Court recognized the trial court's ability to draw conclusions from the evidentiary material before it, provided the inference is sound and supported by the record.

Relevance

Algorithmic causation often requires inference.

For example:

Repeated recommendation → dramatic increase in exposure → predictable increase in harm.

A court may draw reasonable inferences from technical and factual evidence, but those inferences must be logically supported.

Relevance: Strong evidentiary analogy.

32. Case 12 — UAE Federal Supreme Court, Penal Cassation No. 1093/2019

Principle

The Court recognized the trial court's authority to assess and weigh evidence and determine what evidence is reliable and probative.

Relevance

This supports the principle that an algorithmic report does not possess automatic superior evidentiary status.

The court can compare:

algorithmic output;

human testimony;

expert evidence;

documentary evidence;

system logs;

communications.

Relevance: Evidentiary analogy.

33. A Proposed UAE Analytical Test

For future algorithmic-amplification disputes, a UAE civil court could conceptually examine the following factors.

Step 1 — Identify the original harmful conduct

What created the initial harm?

Step 2 — Identify the algorithmic intervention

What did the algorithm do?

Step 3 — Determine control

Who designed, operated, modified or controlled the system?

Step 4 — Determine knowledge

Did the operator know or reasonably have the ability to know about the risk?

Step 5 — Determine foreseeability

Was the resulting harm reasonably foreseeable?

Step 6 — Determine amplification

Did the algorithm materially increase:

audience;

speed;

repetition;

targeting;

economic impact?

Step 7 — Determine causation

Would the harm have occurred at substantially the same scale without the algorithmic amplification?

Step 8 — Examine intervening causes

Did another independent event break the causal chain?

Step 9 — Examine victim contribution

Did the claimant contribute to the damage?

Step 10 — Determine damages

What additional damage resulted from the amplification?

34. Incremental-Harm Theory

A particularly useful approach is to distinguish base harm from incremental harm.

Suppose:

original defamatory publication causes AED 10,000 damage;

algorithmic amplification causes an additional AED 100,000 loss.

The court could conceptually ask:

What damage was caused by the original act, and what additional damage was materially contributed by algorithmic amplification?

This prevents both:

excessive liability for the algorithm operator; and

under-compensation of the victim.

35. Algorithmic Responsibility Matrix

This is not a statutory test but provides a useful analytical framework.

36. Algorithmic Amplification and Damages

Potential damages may include:

Economic loss

lost income;

lost business;

transaction losses;

remediation costs.

Reputational harm

loss of goodwill;

business reputation;

professional reputation.

Privacy harm

unauthorized disclosure;

misuse of personal information.

Property-related loss

Where algorithmic activity causes physical or property damage.

Consequential losses

Where legally recoverable and sufficiently connected to the wrongful conduct.

The claimant must still establish causation and legally recognized damage.

37. Defences

An algorithm operator may argue:

1. Lack of causation

The harmful content itself, rather than the algorithm, caused the loss.

2. Lack of knowledge

The operator had no reasonable basis to anticipate the harm.

3. Intervening act

An independent third party caused the relevant loss.

4. User contribution

The claimant contributed to the damage.

5. Reasonable safeguards

The operator implemented reasonable technical and organizational measures.

6. Lack of control

The defendant did not control the relevant algorithmic function.

7. Contractual limitation

A valid contractual limitation may sometimes be relevant, subject to mandatory law and public policy.

38. Algorithmic Transparency

Transparency is increasingly important because victims may not know:

why content was recommended;

how many times it was recommended;

which users received it;

what variables were used;

whether the algorithm was changed.

This creates an information asymmetry.

The party controlling the algorithm usually possesses much more evidence than the victim.

Consequently, procedural mechanisms concerning disclosure, electronic evidence and expert examination may become critical.

39. Algorithmic Audits

An algorithmic audit may examine:

training data;

recommendation rules;

ranking criteria;

safety filters;

moderation systems;

historical logs;

changes to the model;

error rates;

amplification patterns.

An independent technical expert may then assist the court in determining whether the algorithm materially contributed to the harm.

40. Confidentiality and Trade Secrets

Algorithmic litigation creates another problem.

A defendant may argue:

“Our recommendation algorithm is a trade secret.”

At the same time, the claimant may argue:

“Without access to the algorithmic evidence, I cannot prove causation.”

The court may need to balance:

access to justice;

confidentiality;

trade secrets;

privacy;

cybersecurity.

Possible solutions include:

confidential inspection;

expert-only access;

redacted disclosure;

protective orders;

controlled production of logs.

41. Arbitration

Algorithmic-amplification disputes may also arise in arbitration.

Typical disputes include:

AI software contracts;

SaaS agreements;

advertising agreements;

data-processing agreements;

platform agreements;

algorithm-development contracts.

An arbitral tribunal may need to determine:

contractual responsibility;

technical causation;

evidence authenticity;

expert evidence;

damages;

limitation clauses.

The tribunal should not automatically treat an algorithmic output as conclusive merely because it was produced by sophisticated technology.

42. UAE Civil Law Compared with Traditional Tort Thinking

Traditional civil liability generally focuses on:

Wrongful act → Damage → Causation → Liability

Algorithmic amplification requires a more sophisticated model:

Original act
↓
Algorithmic intervention
↓
Distribution/ranking
↓
Audience expansion
↓
Repeated exposure
↓
Behavioural/economic effect
↓
Damage

This makes causal contribution one of the most important issues in future UAE technology litigation.

43. Key Legal Principle

The most important principle can be summarized as follows:

An algorithm should not be treated as a legal person merely because it makes autonomous recommendations; responsibility generally remains attributable to the natural or juridical persons who designed, controlled, deployed, benefited from, or failed to appropriately supervise the system, subject to the applicable legal rules.

Therefore, “the algorithm did it” should generally not be sufficient as a legal defence.

44. Practical Example

Consider a UAE investment platform.

A fraudulent company posts an investment advertisement promising guaranteed returns.

The platform's algorithm:

identifies the advertisement as highly engaging;

recommends it to financially inexperienced users;

targets users based on browsing behaviour;

repeatedly displays the advertisement;

continues doing so after multiple complaints.

A claimant loses AED 200,000.

The court could investigate:

whether the advertisement itself was unlawful;

whether the platform had knowledge;

whether the algorithm materially increased exposure;

whether the targeting system was foreseeable;

whether complaints were received;

whether reasonable safeguards existed;

whether the claimant relied on the advertisement;

whether other actors caused the loss;

what portion of the damage was attributable to each actor.

This demonstrates why algorithmic amplification is fundamentally a multi-causal civil-liability problem.

45. Major Legal Challenges in UAE

The UAE will increasingly face difficult questions concerning:

1. Autonomous recommendation

When does recommendation become active participation?

2. Algorithmic knowledge

When should an operator be deemed to know what its algorithm is doing?

3. Causation

How much additional damage must an algorithm cause before responsibility attaches?

4. Explainability

How much technical explanation should be required?

5. Evidence

Who should produce algorithmic logs?

6. Trade secrets

How should proprietary algorithms be examined?

7. Cross-border platforms

Which jurisdiction governs a global algorithm?

8. AI agents

Who is responsible when an autonomous AI agent makes a harmful decision?

9. Damages

How should incremental algorithmic harm be quantified?

10. Multiple defendants

How should responsibility be divided between creator, platform, developer and advertiser?

46. Important Distinction: Algorithmic Amplification vs Algorithmic Creation

This distinction will become increasingly important in UAE civil litigation.

47. Overall Legal Position

Under UAE civil-law principles, algorithmic amplification should not be analysed as an entirely separate category of liability. Instead, courts can potentially apply established doctrines concerning:

unlawful harm;

causation;

abuse of rights;

contractual good faith;

multiple causes;

evidentiary reliability;

expert evidence;

damages.

The technological novelty lies primarily in how causation and attribution are proved.

The current UAE Evidence Law is particularly important because algorithmic disputes will frequently depend upon electronic records and technical evidence.

48. Conclusion

Algorithmic amplification of harm responsibility represents a major emerging issue in UAE civil law.

The central legal challenge is to determine when an algorithmic operator moves from being a passive technological intermediary to becoming a material contributor to legally compensable harm.

The strongest framework is to examine:

control over the algorithm;

knowledge of the risk;

foreseeability of harm;

degree of amplification;

causal contribution;

failure to implement safeguards;

economic or other benefit;

abuse of rights;

contractual good faith;

actual and provable damage.

The UAE case law presently available does not yet provide a large body of reported decisions specifically dealing with generative-AI recommendation algorithms or algorithmic amplification. The cases discussed above therefore principally provide foundational analogies concerning abuse of rights, contractual obligations, causation, concurrent causes, attribution, expert evidence and judicial evaluation of proof.

The emerging UAE approach can ultimately be summarized as:

Technology may change the mechanism through which harm occurs, but it does not eliminate the civil-law requirements of attribution, causation, unlawfulness, good faith and compensation.