Civil Law And UAE Future Codification Under AI-Assisted Legal Drafting

1. Introduction

AI-assisted legal drafting means using artificial intelligence to assist with the creation, revision, comparison, translation, harmonisation or analysis of legislation, regulations, explanatory memoranda and related legal materials.

For UAE civil law, this raises a major future question:

Can AI accelerate codification without weakening legislative authority, legal certainty, human accountability and judicial interpretation?

The answer is best understood as AI as a legislative tool, not AI as the source of legal authority.

The UAE's recent recodification is particularly important. Federal Decree-Law No. 25 of 2025 promulgating the Civil Transactions Law entered into force on 1 June 2026 and repealed the 1985 Civil Transactions Law. The UAE Government describes the new law as part of a continuing modernisation of the civil-law framework, aimed at clearer rules, reduced duplication and greater coherence. (UAE Legislation)

This creates an important future model:

HUMAN POLICY → AI-ASSISTED RESEARCH/DRAFTING → HUMAN LEGAL REVIEW → CONSULTATION → LEGISLATIVE APPROVAL → PROMULGATION → JUDICIAL INTERPRETATION → PERIODIC REFORM

2. Meaning of AI-Assisted Codification

AI-assisted codification does not necessarily mean that an AI system writes a Civil Code independently.

It can include AI being used for:

identifying inconsistencies between statutes;

comparing old and new provisions;

detecting duplicated provisions;

analysing judicial decisions;

identifying recurring disputes;

suggesting legislative language;

translating legal provisions;

checking defined terms;

testing cross-references;

mapping conflicts between laws;

analysing consultation responses;

identifying obsolete provisions;

simulating possible interpretations;

maintaining legislative databases.

The final legal authority, however, remains with the constitutionally and statutorily authorised human institutions.

3. Why AI May Change Future UAE Codification

Traditional codification is extremely labour-intensive.

A major civil code requires analysis of:

existing legislation;

court decisions;

commercial practices;

international developments;

academic literature;

regulatory rules;

social developments;

technological changes;

economic requirements.

AI can potentially process large amounts of material much faster.

For example:

10,000 judgments → AI classification → recurring legal problems → human legal analysis → legislative proposal

This could make future amendments more responsive.

But speed creates a corresponding danger:

Faster drafting can also produce faster errors.

4. Fundamental Principle — AI Cannot Replace Legislative Authority

The first principle of future AI-assisted codification should be:

AI may assist legislative reasoning; it cannot independently create binding UAE law.

A legal provision becomes binding through the constitutionally established legislative process and promulgation.

Therefore:

AI draft ≠ law

AI recommendation ≠ legislation

AI prediction ≠ legal rule

AI-generated commentary ≠ authoritative interpretation

This distinction is essential for maintaining the rule of law.

5. Current UAE Codification Context

The new Civil Transactions Law provides a useful example of modern codification.

The UAE Government describes Federal Decree-Law No. 25 of 2025 as a comprehensive restructuring of civil transactions law, intended to:

reorganise rights and obligations;

clarify legal rules;

eliminate duplication;

modernise civil-law provisions;

align civil and commercial regulation;

improve practical application.

The law entered into force on 1 June 2026. (UAE Legislation)

The future question is therefore not simply whether AI can draft another code.

It is:

How can AI make future amendments and recodification more systematic while preserving human legislative judgment?

6. First Development Path — AI-Assisted Legislative Research

AI can analyse:

previous Civil Transactions Law provisions;

new Civil Transactions Law provisions;

Court of Cassation judgments;

DIFC/ADGM jurisprudence;

arbitration decisions;

regulatory legislation;

comparative civil codes.

It can then identify:

contradictory language;

obsolete provisions;

repeated concepts;

undefined terms;

inconsistent terminology;

areas generating repeated litigation.

Future model

LEGISLATION → CASE LAW → AI ANALYSIS → PROBLEM IDENTIFICATION → HUMAN REVIEW → AMENDMENT

This may make future codification more evidence-based.

7. Second Development Path — AI-Assisted Detection of Legislative Gaps

AI can potentially identify recurring disputes where legislation is unclear.

For example, if thousands of cases repeatedly concern:

digital assets;

smart contracts;

AI-generated evidence;

algorithmic liability;

automated transactions;

AI can identify these patterns.

Legislators can then decide whether:

existing law is sufficient;

judicial interpretation is sufficient;

regulations should be amended;

a new statutory provision is necessary.

This is important because:

A recurring dispute does not automatically require a new law.

The human legislative process must decide whether the problem is genuinely legislative.

8. Third Development Path — AI-Assisted Legislative Consistency

One of the strongest applications may be consistency checking.

An AI system could examine a draft Civil Code for:

inconsistent definitions;

duplicate provisions;

conflicting exceptions;

incorrect cross-references;

inconsistent terminology;

inconsistent treatment of similar legal relationships.

For example, if one provision uses:

"electronic record"

and another uses:

"digital record"

AI can flag the difference.

Human legislative experts can then determine whether the terms should remain distinct or be harmonised.

9. Fourth Development Path — AI and Legal Definitions

Codification depends heavily on definitions.

Future legislation may need definitions of:

digital asset;

artificial intelligence;

automated decision;

smart contract;

electronic transferable record;

algorithmic system;

digital identity;

autonomous system.

AI can compare definitions across legislation.

But definitions involve policy and legal judgment.

Therefore:

AI can identify definitional inconsistency; humans must determine the legally appropriate definition.

10. Fifth Development Path — AI-Assisted Comparative Codification

The UAE operates within an international commercial environment.

Future codification may compare:

civil-law jurisdictions;

common-law jurisdictions;

international conventions;

model laws;

arbitration frameworks;

digital-asset legislation.

AI can rapidly compare thousands of provisions.

For example:

UAE Civil Law → French Civil Code → German BGB → Swiss Code → English principles → international model law

But comparative similarity does not mean that foreign law should automatically be adopted.

The UAE must determine whether a foreign rule fits:

UAE constitutional principles;

Islamic legal principles where relevant;

existing UAE legislation;

commercial policy;

social policy;

institutional structures.

11. Sixth Development Path — AI-Assisted Case-Law Integration

Codification should not exist separately from judicial interpretation.

Courts reveal where legal rules create difficulty.

AI could map:

STATUTE → JUDGMENTS → INTERPRETATIONS → CONFLICTS → LEGISLATIVE RESPONSE

This may enable a more continuous relationship between legislation and jurisprudence.

The result could be a form of continuous codification rather than occasional wholesale reform.

12. Case Law 1 — Arabyads Holding Limited v Gulrez Alam Marghoob Alam [2025] ADGMCFI 0032

This is currently one of the most important UAE-region cases for understanding AI and legal drafting/research.

The ADGM Court dealt with legal submissions containing authorities that had not been properly verified and were associated with AI-assisted research.

The Court emphasised that lawyers using AI have a professional duty to check the accuracy of AI-generated research against authoritative sources. The legal representatives were ultimately ordered to pay AED 282,508 in indemnity costs in relation to the wasted-costs application. (jibudocs.com)

Principle

AI assistance does not transfer professional responsibility from the human legal professional to the machine.

Relevance to future codification

The same principle should apply to legislative drafting:

AI-generated provision → human verification → authoritative source checking → legal approval

A legislative drafter should not be able to defend an erroneous provision merely by saying:

"The AI generated it."

13. Case Law 2 — Klesta Eshja & Hair Creators Salon LLC v Salah Masri & Others [2024] DIFC CFI 066

The DIFC litigation involved pleadings containing problematic AI-assisted legal material.

The Court subsequently dealt with consequences including the procedural treatment and costs associated with the defective pleadings. A March 2026 order concerned wasted costs following the earlier proceedings, while later orders continued the procedural management of the matter. (DIFC Courts)

Principle

AI-generated legal material must be subject to:

verification;

procedural control;

accuracy checking;

professional responsibility.

Codification significance

AI-assisted legislative drafting should therefore include:

DRAFT → VALIDATE → VERIFY → HUMAN REVIEW → AUTHORISE

rather than:

PROMPT → PUBLISH

14. Case Law 3 — Oheo Bank v Parker [2025] DIFC CA 006

Oheo Bank v Parker is highly relevant to the future relationship between automated reasoning and legal decision-making.

The DIFC Court of Appeal considered the requirement that a party receive a meaningful opportunity to present its case and examined the importance of procedural fairness and adequate reasoning.

Principle

A legal decision must remain sufficiently reasoned and procedurally fair to permit meaningful review.

Codification significance

This suggests a future safeguard for AI-assisted legislation:

Every AI-generated legislative recommendation should have a traceable reasoning and verification pathway.

A legislative committee should be able to identify:

source material;

assumptions;

proposed rule;

competing alternatives;

reasons for selection;

human approval.

AI-generated output should not become an unreviewable black box. (klgates.com)

15. Case Law 4 — Princeton v Persephone [2026] DIFC ARB 015/027

In Princeton v Persephone, the DIFC Court of First Instance set aside a final arbitral award after finding that the tribunal had decided the case by reference to issues that had not been properly pleaded.

The Court's August 2026 order demonstrates the continuing importance of procedural boundaries: decision-makers must stay within the case that the parties were given an opportunity to address. (DIFC Courts)

Relevance to AI drafting

This principle has a legislative analogue.

An AI system might identify a new legal issue that appears logically connected to a draft provision.

But:

Logical relevance does not automatically equal legislative authority.

A legislative process should identify when a proposal goes beyond the mandate or consultation subject.

16. Case Law 5 — Olan v Obelix [2026] DIFC ARB 053/054

In Olan v Obelix, the DIFC Court considered an application to set aside a DIAC arbitral award under the DIFC Arbitration Law and ultimately dismissed the set-aside application. (DIFC Courts)

Principle

Judicial intervention in arbitral awards operates within defined statutory grounds rather than as an unrestricted appeal on the merits.

Codification significance

AI-assisted legislative review should similarly have defined boundaries.

AI should not become an alternative sovereign policy-maker.

Its role should be defined by:

legislative mandate;

scope;

authorised datasets;

human supervision;

verification;

confidentiality;

auditability.

17. Case Law 6 — Alarabi Investments Limited v Cron AI Ltd [2026] DIFC CFI 030

This case involves an AI-related commercial entity, Cron AI Ltd, before the DIFC Court.

The June 2026 order demonstrates that an AI-related company remains subject to ordinary civil procedural mechanisms, including default judgment, applications to set aside and procedural deadlines. (DIFC Courts)

Principle

The involvement of AI technology does not itself create an autonomous legal system outside ordinary civil procedure.

Codification significance

Future UAE legislation should therefore avoid assuming that AI systems automatically require entirely separate legal categories.

The better sequence is:

AI ACTOR → LEGAL RELATIONSHIP → EXISTING LAW → GAP ANALYSIS → NEW RULE IF NECESSARY

18. Case Law 7 — BAM Higgs & Hill LLC v Affan Innovative Structures LLC [2021] DIFC CFI 106

This complex construction dispute illustrates the importance of:

evidence;

expert analysis;

causation;

contractual obligations;

judicial evaluation.

Principle

Technical expertise assists adjudication, but technical evidence does not itself become the legal judgment.

Codification relevance

AI can similarly assist legislative drafting without becoming the legislative authority.

Thus:

AI is analogous to an extremely powerful drafting/research assistant, not the legislature itself.

19. Case Law 8 — Shihab Khalil v Shuaa Capital PSC [2009] DIFC CFI 017

This case demonstrates the importance of identifying:

legal duty;

breach;

causation;

loss;

legal basis for liability.

Codification significance

AI systems used for legal drafting should be capable of distinguishing:

FACT

from

LEGAL CONCLUSION

and:

POLICY OPTION

from

EXISTING LAW

Otherwise, AI-generated drafting may inadvertently transform an analytical suggestion into apparently binding legal language.

20. Human-in-the-Loop Legislative Architecture

A future UAE AI-assisted codification system could operate as follows:

Stage 1 — Human legislative mandate

The legislature or authorised government body identifies the subject.

↓

Stage 2 — AI research

AI analyses:

legislation;

cases;

regulations;

comparative law;

consultations.

↓

Stage 3 — AI drafting

AI produces possible formulations.

↓

Stage 4 — Legal validation

Human experts test:

constitutional compatibility;

statutory consistency;

terminology;

policy consequences.

↓

Stage 5 — Stakeholder consultation

Experts, businesses, judges and other stakeholders review the proposal.

↓

Stage 6 — Human legislative decision

The competent authority determines the final text.

↓

Stage 7 — Promulgation

The authorised legal process gives the provision binding status.

↓

Stage 8 — Judicial interpretation

Courts determine how the provision applies to actual disputes.

21. AI and the Separation of Legislative Functions

A major future concern is institutional separation.

There should be a distinction between:

AI research

"What does existing law say?"

AI prediction

"What might courts do?"

AI drafting

"What wording could be used?"

Legislative decision

"What should the law provide?"

Judicial interpretation

"What does the enacted law mean in this dispute?"

These are different functions.

The danger arises if AI-generated predictions are silently converted into legislative rules.

22. AI and Legal Certainty

Codification traditionally promotes:

predictability;

consistency;

accessibility;

stability.

AI could improve these objectives through automated consistency checking.

But AI could also undermine certainty if:

outputs vary with prompts;

models change;

training data changes;

sources are not disclosed;

generated provisions contain hidden assumptions;

different agencies use different models.

Therefore future legislative AI should have:

version control;

audit logs;

fixed reference datasets;

source citations;

human approval;

change records;

testing;

archival preservation.

23. AI Hallucination as a Legislative Risk

The Arabyads case demonstrates why hallucination is particularly serious in law.

An AI system may:

invent a case;

misstate a statute;

combine two legal rules;

produce obsolete wording;

attribute a principle to the wrong court;

overlook an amendment.

In ordinary writing, an error may be inconvenient.

In legislation, it can become systemic.

Therefore:

The higher the legal authority of the document, the stronger the verification requirement should be.

24. AI and Legislative Language

Civil codes depend on precise language.

Consider the difference between:

"may"

and

"shall"

or:

"unless otherwise agreed"

and

"unless expressly otherwise agreed."

A small linguistic change can produce significant legal consequences.

AI can help identify linguistic inconsistencies, but human legal drafting remains essential because legal language contains:

policy choices;

institutional assumptions;

historical meanings;

interpretative consequences.

25. AI-Assisted Legislative Translation

This is particularly important for UAE legislation because Arabic is legally authoritative in the federal legislative context while English translations are widely used for commercial and international purposes.

AI can assist with:

Arabic-English translation;

terminology consistency;

cross-version comparison;

detection of omitted phrases.

But:

Translation assistance must not silently alter legal meaning.

The future system should therefore maintain:

SOURCE ARABIC TEXT → AI TRANSLATION → HUMAN LEGAL REVIEW → APPROVED TRANSLATION

26. AI and Legislative Consultation

Future codification may become more participatory through AI-assisted consultation.

AI could classify thousands of submissions into categories:

support;

objection;

ambiguity;

economic concern;

constitutional concern;

implementation problem;

drafting problem.

But classification must not become automatic policy selection.

For example:

10,000 objections

does not necessarily mean:

the proposed rule is legally wrong.

AI can identify patterns; policymakers and legislators determine their significance.

27. AI and Continuous Codification

Traditional model:

Major Code → Long Period → Problems Accumulate → New Code

Future model:

CODE → DATA → CASES → AI ANALYSIS → PROBLEM DETECTION → HUMAN REVIEW → TARGETED AMENDMENT

This could produce continuous or adaptive codification.

The advantage is responsiveness.

The risk is excessive legislative instability.

Therefore:

AI should make codification more responsive without making the law constantly change.

28. AI and Judicial Feedback

Judicial decisions can provide feedback to legislative drafting.

Suppose courts repeatedly interpret a provision in an unexpected manner.

An AI system could detect:

repeated interpretive disputes;

conflicting judgments;

recurring ambiguity;

high litigation frequency.

The legislature could then decide whether clarification is appropriate.

The model becomes:

LAW → CASES → AI ANALYSIS → LEGISLATIVE FEEDBACK → AMENDMENT

This is a form of feedback-controlled codification.

29. AI and Regulatory Fragmentation

The UAE legal system contains:

federal legislation;

emirate-level laws;

financial free-zone regimes;

DIFC laws;

ADGM laws;

sectoral regulations.

AI can potentially create a legislative conflict map showing:

RULE A → RULE B → POSSIBLE CONFLICT → JURISDICTION → PRIORITY → HUMAN REVIEW

This could reduce accidental duplication.

But the existence of a technical conflict does not itself determine which law should prevail.

That remains a legal question.

30. AI and Future Civil-Code Architecture

A future UAE Civil Transactions framework could increasingly contain provisions dealing expressly with:

Digital transactions

Electronic contracting and automated transactions.

Digital assets

Ownership, transfer, security and recovery.

Smart contracts

Contractual validity and automated performance.

AI systems

Civil responsibility and allocation of duties.

Algorithmic harm

Causation and evidentiary rules.

Digital evidence

Authenticity and reliability.

Autonomous systems

Responsibility for system-created harm.

Data-related harm

Contractual and tortious remedies.

Digital inheritance

Succession of digital assets.

Cross-border digital property

Jurisdiction and applicable law.

31. AI Does Not Necessarily Require a New Civil Code

A critical point is:

New technology does not automatically require new codification.

Existing doctrines may already cover many problems.

For example:

AI misinformation

May involve:

misrepresentation → negligence → defamation → contract

AI software failure

May involve:

contract → product liability → negligence

Wrongly transferred cryptocurrency

May involve:

property → unjust enrichment → restitution → tracing

AI-generated legal error

May involve:

professional duty → negligence → procedural consequences

Therefore the legislative question should be:

Is existing law inadequate, or is the problem simply a new application of existing law?

32. Future AI Legislative Safeguards

A robust UAE AI-assisted legislative framework could require:

1. Source traceability

Every important AI recommendation should identify its sources.

2. Human verification

A qualified legal expert verifies important legal propositions.

3. Version control

Every AI-generated draft is archived.

4. Auditability

The legislative institution can reconstruct how the draft was produced.

5. Model governance

The AI system and relevant configuration should be documented.

6. Confidentiality

Sensitive legislative material must be protected.

7. Bias testing

AI outputs should be tested for systematic distortion.

8. Constitutional review

AI-generated proposals must be checked against higher legal norms.

9. Human approval

Binding legislation must be approved through the authorised process.

10. Post-enactment review

Courts and institutions should provide feedback.

33. Future Codification Formula

RESEARCH → CLASSIFY → COMPARE → IDENTIFY GAP → DRAFT → VERIFY → CONSULT → REVIEW → LEGISLATE → PROMULGATE → INTERPRET → MONITOR → REFORM

AI can potentially participate in almost every stage except the exercise of ultimate legislative authority.

34. Important Distinctions

35. Onshore UAE, DIFC and ADGM Must Be Distinguished

This topic requires special caution.

Onshore UAE

Future codification primarily concerns:

federal legislation;

Federal Civil Transactions Law;

federal procedural law;

Federal Supreme Court/court jurisprudence;

emirate-level legislation.

DIFC

DIFC has its own:

laws;

courts;

procedural rules;

common-law influenced jurisprudence.

ADGM

ADGM likewise has its own legal and judicial framework.

Thus, Arabyads, Klesta, Oheo Bank, Princeton and Olan are useful UAE-region authorities illustrating AI/procedural principles, but they are not automatically binding authorities for onshore UAE civil legislation.

36. Key Risks of AI-Assisted Codification

A. Hallucination

False authorities or legal propositions.

B. Hidden bias

Training data may reproduce historical assumptions.

C. Automation bias

Human drafters may accept AI recommendations too readily.

D. Loss of legislative judgment

Policy choices may be disguised as technical recommendations.

E. Excessive uniformity

AI may favour standard language when genuine legal differentiation is necessary.

F. Opacity

Future users may not know why a provision was drafted.

G. Version instability

Changing AI systems could produce inconsistent drafts.

H. Confidentiality

Unreleased legislative material could be exposed.

I. Overfitting to past cases

AI trained on historical disputes may fail to anticipate genuinely new social conditions.

37. Benefits of AI-Assisted Codification

If properly governed, AI may improve:

legislative research;

consistency;

terminology;

comparative analysis;

identification of obsolete provisions;

detection of conflicts;

drafting efficiency;

translation;

accessibility;

consultation analysis;

monitoring of judicial interpretation;

legislative updating.

The central advantage is therefore:

AI can increase the information-processing capacity of the legislative system.

It should not replace legislative judgment.

38. Master Case-Law Table

39. Ultra-Fast Memory Triggers

AI assists; legislature decides.

AI output ≠ law.

Draft ≠ enacted legislation.

Human verification is essential.

Hallucination is a legislative risk.

Source traceability improves legal certainty.

Definitions require human legal judgment.

AI can detect inconsistency; humans decide whether inconsistency exists legally.

AI can identify legislative gaps; legislators decide whether to fill them.

Comparative law is evidence, not automatic UAE law.

Judicial decisions provide feedback to codification.

AI should not become a hidden policymaker.

Legislative authority cannot be delegated merely because drafting is automated.

Reasons and audit trails are important.

Translation requires legal verification.

Confidential legislative data requires protection.

Continuous codification should not become unstable codification.

New technology does not automatically require a new legal category.

Existing civil doctrines should be tested before creating new ones.

Onshore UAE ≠ DIFC ≠ ADGM.

40. Final Conclusion

The future of UAE civil-law codification under AI-assisted legal drafting is best understood as technological augmentation of the legislative process rather than replacement of legislative authority.

The 2025 Civil Transactions Law, effective from 1 June 2026, demonstrates the UAE's continuing commitment to comprehensive civil-law modernisation and greater coherence. (UAE Legislation)

The emerging AI-related cases provide an important warning. Arabyads demonstrates that AI-generated legal material must be verified; Klesta Eshja illustrates the procedural consequences of problematic AI-assisted pleadings; Oheo Bank emphasises meaningful reasoning and procedural fairness; and Princeton demonstrates the importance of keeping adjudication within properly presented issues. (jibudocs.com)

Accordingly, the strongest future model is:

AI RESEARCH → AI-ASSISTED DRAFT → SOURCE VERIFICATION → HUMAN LEGAL ANALYSIS → POLICY REVIEW → CONSULTATION → HUMAN LEGISLATIVE APPROVAL → PROMULGATION → JUDICIAL INTERPRETATION → AI-ASSISTED MONITORING → CONTROLLED REFORM

Final memory line:

“Future UAE codification may use AI to research, compare, draft, test and monitor legal rules, but binding civil law must remain the product of lawful legislative authority, human legal judgment, verification, promulgation and judicial interpretation.”