1. Introduction

Artificial intelligence (AI) is increasingly used in Canadian banking, healthcare, employment, education, insurance, public administration, transportation, consumer services and commercial contracting. These developments raise important questions of civil law concerning negligence, contractual liability, privacy, discrimination, product safety, intellectual property and compensation for harm caused by automated systems.

Comparative Canadian AI law examines how Canada’s federal and provincial legal frameworks address these issues and how they compare with other legal systems, particularly the European Union, the United States, the United Kingdom and civil-law jurisdictions such as Québec.

Canada does not have a single, comprehensive AI liability code governing every private and public use of artificial intelligence. Instead, AI-related disputes are generally assessed under existing legislation, common-law principles, Québec civil law, constitutional requirements where applicable, and sector-specific regulations.

The central legal question is whether existing rules can adequately allocate responsibility when an AI system makes a harmful decision, generates misleading information, processes personal data improperly, or causes physical or economic loss.

2. Meaning and scope of Canadian AI law

Canadian AI law is an interdisciplinary field concerned with the legal consequences of developing, deploying, selling and using AI systems.

Its principal components include:

Contract law: Responsibility for inaccurate AI outputs, service failures, warranties and automated contractual performance.

Tort law: Negligence, duty of care, causation and damages arising from AI-related harm.

Privacy and data protection: Collection, processing, disclosure and retention of personal information used by AI systems.

Human rights law: Discrimination caused or reinforced by algorithmic decision-making.

Consumer protection: Misleading AI-generated representations, unfair commercial practices and defective products.

Intellectual property: Copyright, ownership, training data and infringement involving AI-generated material.

Public and administrative law: Lawfulness, procedural fairness, transparency and review of government decisions involving automated tools.

A fundamental distinction is between the AI system itself and the people or organizations responsible for it. An AI system is not ordinarily treated as an independent legal person that can personally bear civil liability. Responsibility instead depends on the conduct, duties and legal relationships of developers, suppliers, employers, institutions, businesses or public authorities.

3. Structure of the Canadian legal system

A. Federal jurisdiction

Parliament may legislate on matters within federal constitutional authority, including banking, criminal law, telecommunications and certain interprovincial or international commercial activities. Federal privacy legislation also governs specified organizations and activities.

Relevant legislation includes:

Personal Information Protection and Electronic Documents Act (PIPEDA): Governs personal information in commercial activities within its scope and certain interprovincial and international transfers.

Canadian Human Rights Act: Addresses discrimination within federal jurisdiction.

Copyright Act: Governs copyright protection and infringement, including disputes involving AI-generated or AI-assisted works.

Competition Act: Addresses deceptive marketing and other anti-competitive conduct relevant to AI products and algorithmic markets.

Bank Act and applicable financial regulations: May govern institutions deploying AI for credit, fraud detection, risk assessment and other banking activities.

The precise application of each statute depends on the organization, activity and facts. Federal jurisdiction does not mean that all AI activities in Canada are regulated exclusively by Parliament.

B. Provincial and territorial jurisdiction

Provincial and territorial law is especially important for private-law liability, consumer transactions, employment, health services, education and property.

Depending on the province, relevant laws may include:

Provincial privacy legislation.

Human rights codes.

Consumer protection statutes.

Negligence and product-liability principles.

Employment standards and workplace legislation.

Health-information privacy legislation.

Electronic commerce and evidence statutes.

For example, a private employer using an algorithm to screen job applicants may face human rights and privacy obligations under the applicable provincial regime, while a federally regulated employer may be subject to federal law.

C. Québec's civil-law approach

Québec is distinctive because private-law obligations are principally governed by the Civil Code of Québec, rather than the common-law system used for most private-law disputes in the other provinces.

Important provisions include:

Article 1457: General civil liability for failure to comply with standards of conduct imposed by law, custom or circumstances.

Article 1458: Contractual liability for failure to perform contractual obligations.

Articles 1468–1469: Product-manufacturer and supplier liability for safety defects, subject to the statutory requirements.

Articles 35–37: Protection of reputation and privacy, including restrictions on collecting, holding, using and disclosing personal information.

These provisions can apply to AI-related disputes when their respective legal requirements are met. The fact that an AI system is technically complex does not automatically exempt its developer or user from ordinary civil-law obligations.

4. Major legal principles governing AI in Canada

A. Duty of care and negligence

Canadian negligence law can apply when an AI system causes foreseeable harm because of negligent design, testing, implementation, supervision or maintenance.

A claimant generally needs to establish:

A duty of care owed by the defendant.

A breach of the applicable standard of care.

A causal connection between the breach and the loss.

Legally compensable damage.

For example, if a hospital relies on an inadequately tested AI diagnostic tool and a patient suffers injury, liability may depend on the conduct of the hospital, the healthcare professional, the developer and other relevant parties.

The existence of an AI error does not automatically prove negligence. Courts must examine reasonable precautions, the information available at the time, the intended use of the system and the defendant's control over the risk.

B. Contractual liability

AI services are often supplied through software licences, cloud-service agreements, subscription contracts and automated platforms. These contracts may allocate responsibility for inaccurate outputs, interruptions, security breaches and unauthorized uses.

Important questions include:

Was the AI output represented as accurate or reliable?

Did the supplier provide express or implied contractual assurances?

Did the customer use the system for its intended purpose?

Are disclaimers or liability limitations enforceable?

Did a party breach confidentiality, data-processing or security obligations?

Canadian courts generally interpret contractual obligations according to the applicable law and the agreement's language, context and commercial purpose. A clause disclaiming responsibility for an AI output will not necessarily defeat every claim.

C. Privacy and data protection

AI systems may process personal information on a large scale, including photographs, biometric identifiers, employment histories, medical records and online behaviour.

Organizations must comply with applicable privacy legislation and other legal duties. Depending on the circumstances, lawful processing may require meaningful consent, a valid legal basis, reasonable safeguards, appropriate retention practices and limits on secondary use.

Public accessibility does not automatically eliminate privacy protections. Facial-recognition systems, for example, can create new risks when images originally shared for one purpose are collected and used to identify people at scale.

D. Discrimination and algorithmic fairness

An AI system may disadvantage individuals because its training data is unrepresentative, its variables act as proxies for protected characteristics, or its outputs reproduce historical patterns of discrimination.

Canadian human rights legislation may apply to discriminatory decisions in employment, housing, services, education and other protected areas.

A defendant cannot necessarily avoid responsibility by arguing that a computer, rather than a human, made the decision. The relevant inquiry is whether the conduct and resulting decision comply with the applicable legal requirements.

E. Product liability and consumer protection

AI-enabled products, including medical devices, autonomous vehicles and industrial equipment, may raise issues of defective design, inadequate warnings, negligent manufacture and unsafe deployment.

Liability can depend on whether the product was reasonably safe, whether warnings were adequate, whether the defect caused the injury and whether the claimant's use of the product was foreseeable.

Where AI is supplied as a digital service rather than a physical product, the legal analysis may instead emphasize contract, negligence, consumer protection and privacy.

5. Important Canadian case laws

The following decisions illustrate the developing legal framework. Some directly concern AI or algorithmic tools; others establish legal principles that may apply to AI disputes. They should not all be understood as judgments establishing a comprehensive AI-specific cause of action.

Case 1: Ewert v. Canada (Correctional Service), 2018 SCC 30

Court: Supreme Court of Canada.

Facts: Jeffrey Ewert, an Indigenous prisoner, challenged the use of certain psychological and risk-assessment tools that had not been adequately validated for Indigenous offenders.

Legal issue: Whether the Correctional Service of Canada had complied with its statutory obligation to take reasonable steps to ensure the accuracy of information used about offenders.

Decision: The Supreme Court held that the Service breached section 24(1) of the Corrections and Conditional Release Act by failing to take the required reasonable steps to establish the tools' validity when applied to Indigenous offenders.

Principle: Automated assessments used in consequential decisions must not be treated as inherently reliable. Organizations have legal obligations to take reasonable steps to verify information where the governing law requires it.

Relevance to AI: This is a leading Canadian authority on algorithmic reliability, bias and accountability in institutional decision-making. Its statutory holding is specific to the correctional context, but its reasoning is important when evaluating AI-based risk scores and predictive systems.

Case 2: Moffatt v. Air Canada, 2024 BCCRT 149

Court: British Columbia Civil Resolution Tribunal.

Facts: A customer used Air Canada's website chatbot to obtain information about bereavement fares. The chatbot supplied inaccurate information concerning the airline's policy.

Legal issue: Whether the airline could avoid responsibility for misleading information because the information was provided by its chatbot.

Decision: The tribunal found Air Canada liable for negligent misrepresentation in the circumstances and awarded compensation.

Principle: A business may be responsible for misleading information provided through a digital customer-service system. Describing a chatbot as a separate tool does not automatically remove the business's responsibility.

Relevance to AI: This is a particularly important Canadian generative-AI and chatbot liability decision. It illustrates the risks of deploying customer-facing AI without adequate verification, safeguards and correction mechanisms.

Case 3: Zhang v. Chen, 2024 BCSC 285

Court: Supreme Court of British Columbia.

Facts: A lawyer submitted fabricated legal authorities in court materials, with the incident involving citations generated using ChatGPT.

Legal issue: Whether professional obligations of competence and accuracy remain applicable when lawyers use generative AI to prepare legal submissions.

Decision: The court addressed the use of fabricated authorities and emphasized that generative AI cannot substitute for professional expertise and verification.

Principle: Professionals remain responsible for work product they submit, even when technology assists in its preparation.

Relevance to AI: The decision demonstrates that reliance on an AI-generated output is not, by itself, a defence to professional accountability. In civil litigation, unverified AI-generated material can undermine the administration of justice and create potential professional consequences.

Case 4: Ko v. Li, 2025 ONSC 2766, and the subsequent proceeding reported at 2025 ONSC 2965

Court: Ontario Superior Court of Justice.

Facts: Counsel used fabricated case citations in a factum and oral submissions in a civil estates proceeding. The court considered whether the conduct justified a contempt-related show-cause process and examined counsel's professional obligations.

Legal issue: The court considered the consequences of submitting fictitious legal authorities and the duty to verify materials prepared with non-human technology.

Decision: In the subsequent proceeding, the court considered counsel's admissions, apology, corrective steps and other circumstances when addressing the show-cause matter.

Principle: Lawyers must independently verify AI-assisted legal research and cannot rely on an AI system to establish that a cited judgment exists or supports a proposition.

Relevance to AI: These proceedings are important for professional negligence, litigation responsibility and procedural integrity. They also illustrate that the consequences of AI misuse depend on the particular conduct, procedural context and remedial steps taken.

Case 5: Doan v. Clearview AI Inc., 2024 FC 1063

Court: Federal Court of Canada.

Facts: The proposed class proceeding concerned Clearview AI's collection and use of facial images and related information for facial-recognition purposes.

Legal issue: The case raised questions about privacy, biometric information, the legal consequences of large-scale data collection and the requirements for certifying a class proceeding.

Decision: The Federal Court refused to certify the proposed class proceeding.

Principle: AI-related privacy litigation must satisfy the applicable procedural and substantive legal requirements. The seriousness or novelty of the technology does not eliminate the need to establish the legal basis for the claim and meet the relevant certification criteria.

Relevance to AI: The dispute illustrates the difficulties of pursuing collective remedies for large-scale facial-recognition practices. It should not be interpreted as a general judicial endorsement of unrestricted biometric data collection.

Case 6: Thaler v. Commissioner of Patents, 2025 CACP 8

Decision-maker: Canadian Patent Appeal Board.

Facts: The patent application identified an AI system, DABUS, as the inventor of the claimed invention.

Legal issue: Whether an AI system can be recognized as an inventor under Canadian patent law.

Decision: The Board concluded that an AI system cannot be named as an inventor under the Canadian Patent Act. The resulting patent position did not permit a patent to be granted on the basis of AI as the sole inventor.

Principle: Canadian patent law's inventor requirement cannot simply be displaced by describing an AI system as an autonomous creator.

Relevance to AI: The decision is important for innovation businesses, AI developers and patent applicants. It distinguishes the technical ability to generate an invention from the legal requirements for identifying an inventor under Canadian legislation.

Case 7: Crookes v. Newton, 2011 SCC 47

Court: Supreme Court of Canada.

Facts: The dispute concerned whether providing hyperlinks to defamatory material could amount to publication for purposes of defamation law.

Legal issue: Whether a hyperlink, without more, constitutes publication of the linked defamatory content.

Decision: The Supreme Court held that merely referring to defamatory material through a hyperlink does not, by itself, constitute publication of that material.

Principle: Liability for digital communications depends on the legal significance of the conduct, rather than the mere existence of a technical connection to content.

Relevance to AI: Although not an AI case, it offers a useful comparison for disputes involving AI-generated content, automated summaries and links. Courts must distinguish between creating, adopting, communicating and merely referencing content. The case does not determine liability for AI-generated defamation, which requires a separate analysis of the facts and applicable law.

Case 8: CCH Canadian Ltd. v. Law Society of Upper Canada, 2004 SCC 13

Court: Supreme Court of Canada.

Facts: Legal publishers alleged copyright infringement involving materials reproduced through the Great Library's document delivery and self-service photocopying services.

Legal issue: The case examined copyright infringement, originality, authorization and the fair-dealing defence.

Decision: The Supreme Court clarified the Canadian approach to originality and fair dealing and found that the Law Society's relevant dealings were fair on the facts.

Principle: Copyright questions must be resolved through the statutory framework, including originality, the exclusive rights of copyright owners and applicable exceptions.

Relevance to AI: The decision provides foundational copyright principles for AI training and output disputes. It does not decide whether training a model on copyrighted works is fair dealing, nor whether AI-generated material qualifies for copyright protection. Those questions require separate legal analysis.

6. Comparative analysis: Canada and other legal systems

The comparison below identifies broad structural differences. Actual outcomes depend on the specific legislation, court and facts.

A. Canada compared with the European Union

The European Union has adopted a dedicated AI regulatory framework through the AI Act, which imposes obligations according to the type and risk classification of the AI system.

Canada's legal framework has historically relied more heavily on existing privacy, human rights, consumer protection, contract and negligence laws.

The EU approach is more explicitly organized around AI-system risk categories and regulatory compliance obligations. Canada's approach relies more heavily on applying established legal rules to particular technologies and sectors.

The EU model can offer more detailed ex ante compliance requirements, while Canada's framework can provide flexibility in adapting established legal doctrines to new technologies. Neither approach eliminates the need for effective enforcement and remedies.

B. Canada compared with the United States

The United States has a more fragmented combination of federal and state regulation, sector-specific rules, consumer-protection enforcement and private litigation.

Canada likewise has divided jurisdiction, but its constitutional structure and the role of provincial private law differ from the American system.

In both countries, AI-related claims may involve negligent design, misleading representations, discriminatory decisions, privacy violations and copyright infringement. The applicable standards and available remedies may differ substantially by jurisdiction.

C. Canada compared with the United Kingdom

The United Kingdom primarily applies existing legal rules through a sector-specific regulatory approach rather than relying on a single comprehensive AI statute equivalent to the EU AI Act.

Canada and the UK share important common-law concepts, including negligence, contractual interpretation and judicial review. However, their constitutional arrangements, privacy statutes, human rights regimes and regulatory institutions differ.

Canadian courts may therefore draw useful comparisons with UK decisions, but foreign judgments are generally persuasive rather than binding in Canadian proceedings.

7. Civil liability in specific AI applications

A. Healthcare and medical diagnosis

If an AI system incorrectly identifies a disease or recommends unsafe treatment, liability may involve the healthcare professional, hospital, software supplier or medical-device manufacturer.

The key questions include whether the system was adequately tested, whether the professional exercised appropriate judgment, whether warnings were sufficient and whether the error caused the injury.

Professional negligence cannot be established merely by proving that an AI prediction was wrong. The claimant must establish the relevant legal elements.

B. Employment and recruitment

AI systems may rank applicants, assess performance, recommend promotions or assist in termination decisions.

Potential claims include discrimination, breach of privacy obligations, contractual violations and negligence where legally recognized. Employers should assess the system for discriminatory effects and ensure that employment decisions comply with applicable law.

C. Banking, lending and insurance

Automated credit scoring, fraud detection and insurance underwriting can affect access to essential financial services.

A decision may be challenged where it violates applicable anti-discrimination legislation, privacy rules, contractual duties or financial-sector requirements. The existence of a complex algorithm does not itself establish unlawfulness, but neither does complexity excuse a breach of law.

D. Autonomous vehicles and industrial systems

AI-controlled machinery and vehicles can cause physical injury or property damage. Claims may involve product liability, negligence, maintenance failures, inadequate warnings or insurance coverage.

Determining liability may require technical evidence concerning the system's design, operational logs, software updates, sensor performance and the conduct of human operators.

E. Generative AI and misinformation

Generative AI can produce fabricated legal authorities, false commercial statements, misleading medical information and potentially defamatory content.

Responsibility depends on the nature of the content, the parties' conduct, the applicable legal duties and causation. A developer, platform operator and end user may have different responsibilities, and the same facts will not necessarily create liability for all of them.

8. Remedies available under Canadian law

Depending on the legal basis and jurisdiction, remedies may include:

Compensatory damages: For proven economic loss, personal injury, or other legally recognized harm.

Contractual damages: For losses caused by breach of a contractual obligation.

Injunctions: To prevent or restrain unlawful conduct where the applicable requirements are met.

Privacy remedies: Orders or other remedies available under the relevant privacy legislation.

Human rights remedies: Compensation, corrective measures or other orders for established discrimination.

Administrative review: Review or reconsideration of unlawful government decisions, where available.

Intellectual-property remedies: Injunctions, damages or other statutory relief for proven infringement.

Professional discipline: Sanctions or corrective measures where a regulated professional breaches applicable obligations.

Not every remedy is available in every proceeding. Claimants must identify the proper defendant, establish standing and satisfy the substantive and procedural requirements of the relevant law.

9. Major challenges in Canadian AI regulation

A. Attribution of responsibility

Modern AI systems may be developed by one company, supplied by another, integrated by a third party and deployed by a public institution or commercial customer. Determining which party caused the harm may require detailed contractual and technical evidence.

B. Explainability and evidence

Proprietary algorithms and complex models can make it difficult to understand why a particular output was produced. Litigation may require expert evidence, access to relevant records, disclosure of system documentation and careful treatment of confidential information.

C. Bias and unequal outcomes

A system may produce systematically inaccurate or discriminatory results even when no individual intended to discriminate. The legal assessment must account for the applicable statutory requirements, the decision's effects and the evidence supporting the claim.

D. Cross-border jurisdiction

AI services often process data in several countries and rely on internationally distributed suppliers. This creates questions about applicable law, jurisdiction, contractual choice-of-law clauses and enforcement of judgments.

E. Rapid technological development

Legal rules often develop more slowly than AI capabilities. Courts must apply established legal standards to unfamiliar technical circumstances without assuming that either technological novelty or technical complexity changes the fundamental requirements of liability.

10. Future direction of Canadian AI law

Several developments are likely to shape Canadian AI law:

Greater judicial attention to algorithmic transparency and evidentiary disclosure.

More litigation involving automated discrimination and biometric surveillance.

Increased scrutiny of AI-generated material in professional practice and court proceedings.

More detailed contractual allocation of AI risks between suppliers and customers.

Greater use of independent testing, audits and human oversight in high-impact applications.

Continued debate over copyright, model training, AI-generated works and inventor recognition.

Further clarification of the relationship between federal AI policy and provincial or territorial private-law remedies.

The Federal Court's September 2025 interim principles for its own use of AI illustrate the importance of accountability, accuracy, transparency, non-discrimination, cybersecurity and human oversight in judicial administration. Those principles govern the Court's approach and should not be mistaken for a universal statutory code applicable to every Canadian organization.

11. Conclusion

Canadian AI law is best understood as an evolving combination of civil liability, contractual obligations, privacy protection, human rights, intellectual property and public-law controls. Its comparative character arises from the coexistence of federal and provincial jurisdiction, Québec's civil-law system, and the influence of foreign regulatory developments.

The eight cases discussed above provide a foundation for understanding the emerging jurisprudence. Ewert demonstrates the importance of validating consequential assessment tools; Moffatt illustrates responsibility for misleading chatbot information; Zhang and Ko address professional accountability for AI-generated legal material; Doan highlights procedural challenges in AI privacy litigation; Thaler addresses AI inventorship; and Crookes and CCH Canadian supply foundational principles relevant to digital communications and copyright.

The central principle is that using AI does not automatically transfer legal responsibility from a person or organization to a machine. Liability continues to depend on the applicable legal duties, the conduct of the parties, the evidence of causation and the remedies authorized by law. Comparative analysis helps identify different regulatory approaches, but each Canadian dispute must ultimately be resolved under the law governing that particular relationship and activity.