Civil Law and UAE Algorithmic Bias Civil Claims

1. Introduction

Algorithmic bias occurs when an automated or AI-based system systematically produces unfair, unequal, discriminatory, or otherwise unjustified outcomes for particular individuals or groups. In civil-law disputes, this can arise when algorithms are used for credit scoring, recruitment, insurance pricing, fraud detection, employment decisions, customer profiling, medical-risk assessment, government services, automated contracting, or litigation-support systems.

In the UAE, there is not yet a single, comprehensive civil-liability regime specifically titled “algorithmic bias liability.” Therefore, a civil claim arising from algorithmic bias would generally have to be constructed from established principles of fault, unlawful conduct, contractual liability, professional negligence, causation, damage, compensation, evidence, procedural fairness, and abuse of discretion, together with applicable sector-specific regulation.

The UAE's new Civil Transactions Law, Federal Decree-Law No. 25 of 2025, is particularly relevant because it modernises the UAE civil-law framework and provides the general civil-law foundation against which emerging technological disputes can be analysed.

2. Meaning of Algorithmic Bias

Algorithmic bias may occur when an AI or automated decision system:

• produces systematically unequal outcomes;
• relies upon biased historical data;
• uses inappropriate variables as proxies for protected or sensitive characteristics;
• disproportionately rejects applications from a particular population;
• generates inaccurate risk classifications;
• reproduces human discrimination embedded in training data;
• produces different outcomes for similarly situated persons;
• is insufficiently tested before deployment; or
• continues producing known discriminatory outcomes despite warnings.

Example

Suppose a financial institution uses an AI credit-scoring system.

Two applicants have substantially similar financial circumstances, but the algorithm repeatedly assigns one category of applicants a substantially higher risk score because the training data contains historical patterns that indirectly disadvantage them.

If this causes:

• rejection of credit,
• increased financing costs,
• cancellation of a contractual opportunity,
• reputational damage,
• financial loss,

the affected person may attempt to establish a civil claim.

The central legal question becomes:

Can the claimant connect the biased algorithmic process to an actionable legal wrong, legally recognised damage, and causation?

3. Basic UAE Civil-Law Structure

An algorithmic-bias claim can generally be analysed through five questions:

The important point is that the existence of an algorithmic disparity by itself does not automatically establish civil liability. The claimant ordinarily needs to connect the algorithm's operation to a legally actionable breach and compensable harm.

4. Potential Defendants

Depending upon the circumstances, responsibility may potentially involve several actors.

A. AI developer

A developer may face allegations where:

• the system was negligently designed;
• known bias was not addressed;
• testing was inadequate;
• training data was materially defective;
• the developer made inaccurate representations about system reliability.

B. AI vendor

A vendor may become relevant where contractual warranties concerning accuracy, compliance, testing or performance were breached.

C. Employer

Where an employer uses an algorithm for:

• recruitment,
• promotion,
• dismissal,
• performance scoring,

the employer may face claims concerning the decision and its consequences.

D. Bank or financial institution

Algorithmic credit scoring may create disputes concerning:

• financing rejection;
• pricing;
• risk classification;
• fraud detection;
• account restrictions.

E. Insurance company

AI-based underwriting can potentially produce disputes involving:

• premiums;
• risk classifications;
• coverage decisions;
• claims assessment.

F. Public authority

Where an automated government decision causes civil prejudice, the dispute may additionally involve administrative-law principles, including legality, reasoning, proportionality and abuse of power.

5. Algorithmic Bias and Fault

The traditional civil-law concept of fault is important because an AI system does not necessarily possess independent legal responsibility simply because it produced the discriminatory result.

The investigation may instead focus on human or corporate conduct:

Who designed the system? → Who trained it? → Who deployed it? → Who monitored it? → Who knew about the bias? → Who had the ability to correct it?

Potential forms of fault include:

1. negligent system design;
2. inadequate testing;
3. failure to monitor;
4. failure to investigate complaints;
5. reliance upon defective data;
6. failure to correct known discriminatory outcomes;
7. inadequate human supervision;
8. negligent procurement of an AI system;
9. misleading representations concerning accuracy;
10. unreasonable reliance upon automated outputs.

Thus, algorithmic bias can be converted into a traditional civil-law question:

Was the defendant's conduct, in designing, procuring, deploying or relying upon the algorithm, legally wrongful or negligent and causally connected to the claimant's damage?

6. Contractual Algorithmic-Bias Claims

Algorithmic bias can also create contractual liability.

For example, a company may contract with an AI provider for a recruitment system that is represented as:

• accurate;
• compliant;
• unbiased;
• professionally tested;
• suitable for a particular purpose.

If the system materially fails those contractual specifications, the dispute may be framed as a breach of contract rather than purely as an AI-discrimination claim.

Relevant issues may include:

• contractual warranties;
• representations;
• service-level obligations;
• indemnification;
• limitation-of-liability clauses;
• confidentiality;
• data-quality obligations;
• audit rights;
• compliance obligations.

The contract therefore becomes an important mechanism for allocating algorithmic risk.

7. Causation: The Most Difficult Issue

A claimant may show that an algorithm produced a discriminatory result but still have difficulty proving that the algorithm caused the legally compensable loss.

For example:

AI system → lower credit score → loan rejected → business opportunity lost.

The claimant may have to establish each link.

The defendant might argue:

• the applicant would have been rejected anyway;
• other financial circumstances justified the decision;
• the algorithm's output was only one factor;
• the alleged loss was speculative;
• the business opportunity was uncertain.

This makes causal evidence and counterfactual analysis particularly important.

A useful litigation question is:

What would have happened if the biased algorithm had not been used?

That question can involve statistical evidence, expert evidence, alternative decision models and documentary records.

8. Algorithmic Bias and Loss of Opportunity

The UAE Court of Cassation has recognised the legal possibility of compensation for a lost opportunity.

In Civil Cassation No. 880 of 2021, the Court recognised that compensation may extend to material damage and that compensation for a lost opportunity may be available where the loss is legally established.

This principle can be important in algorithmic-bias litigation.

Example

Suppose an AI recruitment system incorrectly rejects a candidate.

The candidate cannot necessarily prove:

“I definitely would have obtained the job.”

But evidence may establish:

• the candidate satisfied the qualifications;
• the candidate would ordinarily have proceeded to the next stage;
• the algorithm incorrectly excluded the candidate;
• the lost opportunity had real economic significance.

The claimant may therefore attempt to frame the loss as a lost opportunity, subject to proving the necessary elements.

9. Algorithmic Evidence and Expert Evidence

Algorithmic-bias disputes are technically complex.

Courts may encounter:

• source-code evidence;
• training datasets;
• model documentation;
• audit reports;
• statistical analyses;
• validation reports;
• system logs;
• expert reports;
• automated decision records.

The UAE Court of Cassation has repeatedly emphasised that expert evidence does not automatically determine the case.

Civil Cassation No. 647 of 2021

The Court held that a judgment must demonstrate careful examination of the facts and evidence and must address a material defence capable of changing the outcome.

This principle is highly relevant where a party argues:

“The algorithm classified me as high-risk, therefore the decision was correct.”

The court cannot simply treat the algorithmic classification as conclusive. It must examine the evidentiary foundation and the opposing party's material objections.

10. Case Law 1 — UAE Civil Cassation No. 647 of 2021

Principle

The UAE Court of Cassation held that judicial reasoning must demonstrate a comprehensive understanding of the facts and evidence. A material defence supported by documents and capable of changing the court's conclusion must be considered.

Application to algorithmic bias

If a claimant presents evidence showing:

• unequal algorithmic outcomes;
• biased training data;
• statistical disparity;
• improper variables;
• defective system validation,

the court should not simply accept the defendant's statement that:

“The computer made the decision.”

The material defence concerning algorithmic bias requires judicial examination.

Significance

This case supports the principle of human judicial evaluation of automated evidence.

11. Case Law 2 — UAE Commercial Cassation No. 215 of 2020

In Commercial Cassation No. 215 of 2020, the Court held that a court relying upon an expert report must have a reasoned basis for doing so. Merely referring to an expert report without explaining its conclusions or dealing with a meritorious defence can render the judgment deficient.

Application

An AI-bias defendant may submit an expert report stating:

“The algorithm is statistically reliable.”

The claimant may respond:

• the wrong dataset was tested;
• the relevant subgroup was excluded;
• the test measured overall accuracy rather than subgroup accuracy;
• the model contained a proxy variable;
• the expert failed to test alternative explanations.

The court should not simply adopt the expert's conclusion without examining those objections.

Principle

Technical expertise assists the court; it does not replace judicial reasoning.

12. Case Law 3 — UAE Commercial Cassation No. 767 of 2021

In Commercial Cassation No. 767 of 2021, the Court emphasised that an expert's role concerns factual and technical matters, while legal questions remain for the court. The trial court may assess expert work, but its assessment must concern the substance of the dispute and be supported by cogent reasons.

Application to algorithmic bias

An AI expert may determine:

• whether statistical disparity exists;
• whether a model produces different error rates;
• how a dataset was constructed;
• whether the model behaves differently between groups.

But the expert cannot finally decide:

“The defendant is legally liable.”

That is a judicial question.

Importance

This creates a useful division:

AI system → technical evidence

Expert → technical interpretation

Court → legal responsibility

13. Case Law 4 — UAE Administrative Cassation No. 212 of 2021

In Administrative Cassation No. 212 of 2021, the UAE Federal Supreme Court stressed that judicial decisions must demonstrate careful examination of facts and evidence, provide reasons, and address material defences. It also considered the limits of administrative disciplinary discretion and the need to avoid abusive exercise of authority.

Application

This is especially important where government bodies use automated decision-making.

Suppose an authority uses an algorithm to:

• rank applicants;
• determine risk;
• identify suspected violations;
• recommend sanctions.

An affected person could potentially challenge an automated outcome where the decision-making process fails to satisfy applicable requirements of legality, reasoning, procedural fairness or proportionality.

Principle

Automated administration does not eliminate administrative responsibility.

14. Case Law 5 — UAE Administrative Cassation No. 891 of 2019

In Administrative Cassation No. 891 of 2019, the Court considered the limits of administrative discretion and emphasised that discretionary authority cannot be exercised abusively.

Application to algorithmic decision-making

An algorithm may produce a numerical risk score.

For example:

Risk score = 92/100 → automatic adverse decision.

But a high numerical score does not necessarily make the resulting decision legally unchallengeable.

A public authority may still need to consider:

• individual circumstances;
• reliability of the information;
• proportionality;
• purpose of the decision;
• whether the automated outcome is reasonable.

Principle

Algorithmic discretion remains subject to legal limits when exercised by a legally responsible authority.

15. Case Law 6 — UAE Civil Cassation No. 79 of 2020

In Civil Cassation No. 79 of 2020, the Court stated that a material defence capable of changing the outcome must be considered. The Court also emphasised that an admission should generally be evaluated as a whole rather than selectively.

Relevance to AI-generated or algorithmically processed evidence

Suppose an AI system analyses a person's communications and produces:

“Applicant admitted financial misconduct.”

The claimant may argue that the AI extracted one sentence while ignoring the surrounding context.

The reasoning in Cassation No. 79 of 2020 supports careful consideration of the complete evidentiary context rather than allowing an isolated automated interpretation to determine the case.

Principle

Context cannot be sacrificed merely because an automated system has produced a simplified conclusion.

16. Case Law 7 — UAE Civil Cassation No. 880 of 2021

As discussed above, Civil Cassation No. 880 of 2021 is particularly important concerning damages.

The Court recognised:

• compensation for established material damage;
• compensation for present and future damage where legally established;
• compensation for loss of opportunity. 

Algorithmic-bias application

Potential losses could include:

• lost employment opportunity;
• lost financing opportunity;
• additional financing costs;
• lost commercial opportunities;
• business losses;
• certain reputational or consequential harm where legally established.

The claimant must nevertheless prove the legally relevant damage and causal connection.

17. Case Law 8 — UAE Commercial Cassation No. 240 of 2021

In Commercial Cassation No. 240 of 2021, the Court dealt with objections to an expert report and emphasised that material objections must be properly examined where they could affect the result. The failure to deal adequately with such objections can amount to deficient reasoning and infringement of the right of defence.

Algorithmic significance

An algorithmic-bias claimant may challenge:

• the training dataset;
• statistical methodology;
• model validation;
• sampling methodology;
• expert assumptions;
• accuracy measurements.

The court should not treat an expert's algorithmic assessment as immune from challenge.

18. What Must a Claimant Prove?

A practical algorithmic-bias civil claim may require evidence of the following:

1. Automated decision

Prove that an algorithm or AI system materially influenced the decision.

2. Defendant's responsibility

Identify who:

• developed;
• supplied;
• controlled;
• deployed;
• supervised; or
• relied upon

the system.

3. Bias

Show statistically or factually that the system produced an unjustified disparity or otherwise problematic outcome.

4. Legal wrong

Connect the bias to:

• breach of contract;
• negligence;
• unlawful conduct;
• professional fault;
• abuse of discretion;
• breach of applicable regulatory obligations;
• another legally recognised basis of liability.

5. Causation

Demonstrate that the algorithm materially caused the adverse decision.

6. Damage

Establish actual legally compensable loss.

19. Evidence in Algorithmic-Bias Litigation

The following evidence can become important:

20. Difference Between Bias and Mere Error

This distinction is essential.

Ordinary algorithmic error

An algorithm incorrectly rejects an applicant because of a technical error.

Algorithmic bias

The system systematically produces disproportionately adverse outcomes for a particular category because of problematic data, variables, methodology or deployment.

Both can potentially create civil claims, but bias requires more than proving that the system made one incorrect decision.

21. Direct and Proxy Bias

Algorithmic discrimination can occur even when the system does not expressly use a sensitive characteristic.

For example, the system may not use a particular personal characteristic directly but may use variables that strongly correlate with it.

This is commonly described as proxy discrimination.

Potential proxy variables can include:

• geographical information;
• purchasing patterns;
• educational history;
• employment history;
• communication patterns;
• behavioural characteristics.

The civil-law question becomes whether the use of such variables constitutes legally actionable wrongful conduct in the particular context.

22. Human-in-the-Loop Principle

A strong governance model is:

Algorithmic recommendation → human review → explanation → opportunity to challenge → final decision

rather than:

Algorithm → automatic decision → no explanation → no review

Human involvement is particularly important where decisions affect:

• employment;
• housing;
• financing;
• insurance;
• healthcare;
• public services;
• legal rights.

The UAE case law on reasoning and material defences strongly supports the broader proposition that technically generated conclusions should not prevent meaningful judicial or administrative examination.

23. Algorithmic Bias and Burden of Proof

One practical difficulty is information asymmetry.

The claimant may not have access to:

• source code;
• training datasets;
• model weights;
• internal validation reports;
• decision logs.

The defendant may possess almost all relevant technical evidence.

Consequently, litigation can involve disputes over:

• disclosure;
• expert examination;
• confidentiality;
• trade secrets;
• data protection;
• forensic inspection.

This makes procedural management extremely important.

24. Remedies

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

A. Compensation

For proven material loss and other legally compensable damage.

B. Loss-of-opportunity compensation

Where the claimant can establish a legally recognised lost opportunity.

C. Contractual remedies

Where algorithmic performance violates contractual obligations.

D. Corrective action

Depending on the legal context, the claimant may seek correction or reconsideration of an automated decision.

E. Annulment or judicial review

Particularly relevant to administrative decisions.

F. Injunctive or preventive relief

Where continuing conduct threatens legally protected interests and the applicable procedural requirements are satisfied.

25. Defences Available to the Defendant

A defendant may argue:

1. there was no statistically significant bias;
2. the variable was objectively justified;
3. the algorithm was not the decisive cause;
4. human decision-makers independently reviewed the result;
5. the claimant would have received the same result without the algorithm;
6. there was no legally compensable damage;
7. the alleged loss is speculative;
8. the defendant complied with contractual obligations;
9. the AI provider, rather than the defendant, controlled the relevant function;
10. the claimant cannot establish causation.

Therefore, algorithmic-bias litigation is likely to become heavily dependent upon technical evidence plus traditional civil-law proof.

26. Important Distinction: Algorithmic Bias Is Not Automatically a Tort

It would be incorrect to state:

“Any biased algorithm automatically creates civil liability.”

The stronger legal approach is:

Algorithmic bias becomes a civil-liability issue when the biased operation can be connected to a legally actionable breach and legally compensable harm.

This distinction is important because statistical disparity, without further legal analysis, may not by itself establish every element of a civil claim.

27. Relationship with the UAE's Modern Civil-Law Framework

The new UAE Civil Transactions Law is part of the broader modernisation of UAE private law. Its opening provisions establish a hierarchy for resolving civil matters, including statutory provisions, applicable Sharia principles where the statute does not resolve the matter, custom subject to legal limits, and principles of natural law and justice where necessary.

This is relevant to algorithmic disputes because technology can produce factual situations that were not specifically contemplated when older legal provisions were drafted.

Accordingly, courts may have to apply established civil principles to new technological facts.

28. Algorithmic Bias in Different Sectors

29. UAE Case-Law Principles Relevant to Algorithmic Bias

30. Practical Litigation Framework

A claimant bringing an algorithmic-bias claim could structure the case as follows:

Step 1 — Identify the algorithm

Determine exactly which automated system affected the claimant.

Step 2 — Identify the decision-maker

Determine whether the final decision was:

• completely automated;
• partially automated; or
• merely assisted by AI.

Step 3 — Obtain the decision record

Identify:

• inputs;
• outputs;
• scoring;
• classification;
• human intervention.

Step 4 — Establish the alleged bias

Use:

• statistical analysis;
• comparable cases;
• historical data;
• expert evidence.

Step 5 — Establish legal duty

Identify the applicable:

• contract;
• civil obligation;
• professional duty;
• administrative obligation;
• sector-specific regulation.

Step 6 — Establish causation

Show that the biased algorithm materially contributed to the adverse result.

Step 7 — Establish damage

Quantify:

• direct financial loss;
• additional costs;
• lost opportunity;
• other legally compensable damage.

Step 8 — Challenge the defendant's expert

Test:

• dataset;
• methodology;
• assumptions;
• statistical significance;
• model validation.

Step 9 — Seek appropriate remedy

The remedy should correspond to the established legal wrong and damage.

31. Broader Legal Principle

The UAE cases discussed above collectively support an important emerging principle:

Automation does not eliminate accountability.

An algorithm may perform the calculation, classification or recommendation, but civil responsibility must ultimately be traced to a legally responsible person or entity.

Similarly:

Technical complexity does not remove the court's duty to examine evidence and material defences.

The reasoning in 647/2021, 215/2020, 767/2021, 212/2021, 79/2020 and 240/2021 is particularly valuable here.

32. Conclusion

Civil Law and UAE Algorithmic Bias Civil Claims represents an emerging intersection between traditional civil liability and artificial intelligence.

The UAE does not need a standalone “algorithmic bias tort” for existing civil-law principles to become relevant. Depending on the facts, a claim can potentially be constructed around:

• fault or negligence;
• contractual breach;
• professional responsibility;
• unlawful conduct;
• administrative abuse of discretion;
• causation;
• material damage;
• loss of opportunity;
• defective expert evidence; and
• failure to provide adequate human and judicial scrutiny.

The most important lesson from the UAE jurisprudence is that an algorithm's output should be treated as evidence or a decision-making input, not as an unquestionable legal conclusion. The courts retain responsibility for evaluating the evidence, considering material defences, determining causation and deciding whether the claimant has established legally compensable harm.

Thus, the developing UAE model can be summarised as:

Algorithmic output → technical verification → human/legal scrutiny → proof of wrongful conduct → causation → damage → appropriate civil remedy.