1. Meaning

HR chatbot negligence arises when an employer, HR department, recruitment platform, or HR-tech provider uses a chatbot to provide information or guidance to employees/applicants and the chatbot gives false, incomplete, misleading, or materially inaccurate information, while the organisation fails to take reasonable precautions to prevent such errors.

The problem becomes more serious when an employee or applicant reasonably relies on the chatbot's answer and suffers financial or employment-related loss.

For example:

An employee asks an HR chatbot: “If I resign today, will I receive my accrued annual leave payment?”
The chatbot incorrectly says “No.”
The employee therefore delays resignation, loses another job opportunity, and suffers financial loss.

The legal question becomes: Can the employer be liable for the chatbot's answer even though no human HR employee personally gave the advice?

The developing answer is potentially yes, particularly where the organisation presented the chatbot as an official HR information channel and the employee's reliance was reasonably foreseeable.

2. Why HR chatbots create a negligence risk

Traditional HR advice is generally provided by:

• HR managers;
• employment lawyers;
• payroll staff;
• supervisors; or
• written employment policies.

A chatbot changes the situation because it can provide immediate answers without a human checking every response.

Potential errors include:

1. incorrect information about salary;
2. incorrect holiday entitlement;
3. wrong notice-period information;
4. incorrect redundancy calculations;
5. inaccurate disciplinary procedure information;
6. misleading information about sick leave;
7. incorrect parental-leave information;
8. wrong pension information;
9. incorrect grievance procedures;
10. inaccurate information about termination rights.

If the employee reasonably believes that the chatbot represents the employer, the employer may have difficulty arguing:

“The chatbot said it, not us.”

The leading modern illustration is Moffatt v Air Canada, where a tribunal rejected an attempt to separate the company from information supplied through its chatbot.

3. The central legal concept: negligent misstatement

The most relevant doctrine is generally negligent misstatement, rather than ordinary physical-injury negligence.

A simplified test is:

A. Duty of care

Was there a sufficiently close relationship between the organisation and the person receiving the information?

B. False or misleading representation

Did the chatbot provide information that was inaccurate, incomplete or misleading?

C. Lack of reasonable care

Did the organisation fail to take reasonable steps to ensure the chatbot's information was accurate?

D. Reasonable reliance

Did the employee/applicant reasonably rely upon the information?

E. Causation

Did that reliance cause the person's loss?

F. Recoverable damage

Was the resulting loss legally recoverable?

This structure closely resembles the five-part negligent-misrepresentation analysis applied in Moffatt v Air Canada.

4. Case Law 1 — Moffatt v Air Canada (2024)

Facts

Jake Moffatt used Air Canada's website chatbot concerning bereavement fares.

The chatbot gave him information suggesting that he could obtain the relevant fare reduction retrospectively. He relied upon that information and purchased his tickets.

Air Canada subsequently refused the reduction because the actual policy did not permit retrospective applications.

Moffatt brought a claim.

Decision

The British Columbia Civil Resolution Tribunal found Air Canada liable for negligent misrepresentation.

The tribunal considered:

• the commercial relationship between Air Canada and Moffatt;
• the inaccurate chatbot information;
• Moffatt's reliance;
• whether that reliance was reasonable; and
• the resulting financial loss.

The tribunal rejected Air Canada's attempt to distance itself from the chatbot. The chatbot was part of Air Canada's website, and the company was responsible for information communicated through its website.

Importance for HR

This is the most directly relevant modern chatbot case.

An employer operating an HR chatbot could face a similar argument:

“The employee should have known that the chatbot might be wrong.”

That argument becomes considerably weaker where:

• the chatbot is located on the official HR portal;
• it is branded as the employer's HR assistant;
• it provides specific employment information;
• employees are encouraged to use it;
• no disclaimer warns that answers are unreliable; and
• the employee has no obvious reason to doubt the answer.

The tribunal awarded approximately C$650 in substantive damages, together with interest and fees.

5. Case Law 2 — Hedley Byrne & Co Ltd v Heller & Partners Ltd (1964)

This is the foundational English authority concerning negligent statements causing economic loss.

The House of Lords recognised that liability can arise where one person gives information or advice in circumstances involving an assumption of responsibility and another reasonably relies upon it.

The case established the importance of:

• special relationship;
• assumption of responsibility;
• reasonable reliance; and
• economic loss.

Application to HR chatbots

Suppose an employer's HR chatbot tells an employee:

“You are entitled to £8,000 under the company's redundancy scheme.”

The employee relies upon the statement when deciding whether to accept redundancy.

If the information was wrong and the employer had represented the chatbot as an authoritative HR tool, the principles of Hedley Byrne become relevant.

The critical issue would be whether the circumstances establish sufficient responsibility and reliance.

Key principle:

A careless statement can potentially create liability where it is reasonably relied upon in a relationship sufficiently close to justify a duty of care.

6. Case Law 3 — Caparo Industries plc v Dickman (1990)

Caparo Industries plc v Dickman is important for determining whether a duty of care exists.

The House of Lords emphasised:

1. reasonable foreseeability;
2. proximity; and
3. whether it is fair, just and reasonable to impose a duty.

HR chatbot application

Consider an employer providing a chatbot directly to its employees.

Foreseeability

It is foreseeable that employees will rely upon official HR information.

Proximity

The relationship between employer and employee is substantially closer than a relationship between a company and an unknown member of the public.

Fair, just and reasonable

An organisation that deliberately introduces an HR chatbot and encourages employees to use it may have a stronger responsibility to ensure that its outputs are reasonably reliable.

Therefore, Caparo can strengthen the argument for a duty of care in an employment context.

7. Case Law 4 — Esso Petroleum Co Ltd v Mardon (1976)

Esso Petroleum Co Ltd v Mardon is an important authority concerning reliance upon information supplied by a party possessing particular expertise.

The case concerned a representation regarding the expected business performance of a petrol station. The information was relied upon when entering into the commercial arrangement.

The court recognised liability arising from inaccurate information supplied in circumstances where the maker possessed relevant expertise and the recipient relied upon that expertise.

HR chatbot application

An employer's HR chatbot possesses a different type of "expertise": it is presented as a source of official organisational HR information.

For example:

Employee: “What is my contractual notice period?”

Chatbot:

“Your notice period is two weeks.”

The employee relies on this answer and accepts another job beginning two weeks later.

The actual contract requires eight weeks' notice.

The employer could potentially face an argument that:

• the chatbot was presented as an authoritative HR resource;
• the employer controlled the information system;
• reliance was foreseeable;
• the answer was incorrect; and
• the employee suffered economic loss.

The stronger the employer's representation of the chatbot's authority, the stronger the reliance argument may become.

8. Case Law 5 — Spring v Guardian Assurance plc (1994)

This is particularly important because it is an employment-related negligent-misstatement case.

The House of Lords held that an employer/former employer could owe a duty of care when providing an employment reference.

The case concerned inaccurate information about an employee that adversely affected his employment prospects. The House of Lords recognised a duty to take reasonable care when preparing such a reference.

HR chatbot application

Imagine an HR chatbot automatically answers a prospective employer's verification request:

“Employee was dismissed for misconduct.”

But the employee had actually resigned and had never been dismissed for misconduct.

The statement could cause:

• loss of employment;
• loss of earnings;
• reputational harm;
• loss of career opportunities.

Spring demonstrates that employment information can generate a duty of care where inaccurate information causes foreseeable economic consequences.

It is therefore highly relevant to automated:

• employment verification;
• reference systems;
• disciplinary records;
• performance summaries; and
• recruitment chatbots.

9. Case Law 6 — Smith v Eric S Bush (1990)

In Smith v Eric S Bush, the House of Lords considered liability arising from reliance upon professional information and the effect of disclaimers.

The case recognised that a person may owe a duty where they know that another person is likely to rely upon their information. It also considered whether exclusion clauses/disclaimers could reasonably exclude liability.

HR chatbot application

An employer might attempt to protect itself by placing a message:

“Information supplied by this chatbot is for general information only and should not be relied upon.”

But a disclaimer does not necessarily solve every problem.

Its effectiveness may depend upon:

• how prominently it was displayed;
• whether the employee actually saw it;
• the nature of the information;
• the employee's sophistication;
• whether the organisation encouraged reliance;
• applicable contractual/statutory rules; and
• whether excluding liability is legally reasonable.

Therefore, merely putting “AI may make mistakes” at the bottom of an HR chatbot interface should not automatically eliminate liability.

10. Case Law 7 — Mata v Avianca, Inc. (2023)

Although Mata v Avianca is not an employment-liability case, it is extremely useful for understanding human responsibility for AI-generated information.

Lawyers relied on ChatGPT-generated legal research containing fabricated authorities. The court sanctioned the lawyers after the authorities were discovered to be false.

Principle relevant to HR

The important lesson is:

Use of AI does not automatically eliminate the human user's professional responsibility.

In an HR environment, an employer cannot necessarily say:

“The AI made the mistake.”

If HR personnel knowingly deploy a system that produces potentially consequential employment information, reasonable governance may require:

• verification;
• testing;
• human escalation;
• monitoring;
• correction mechanisms; and
• audit trails.

Thus, Mata supports the broader proposition that reliance upon AI does not necessarily transfer legal responsibility away from the human or organisation deploying it.

11. Reliance is the heart of the claim

Not every chatbot mistake produces liability.

The employee generally needs to establish reasonable reliance.

Strong reliance

An employee asks:

“Does company policy give me 30 days' paid parental leave?”

The chatbot says:

“Yes.”

The employee plans leave on that basis.

The answer is wrong.

This is stronger because the chatbot was specifically asked about an HR matter and gave a definite answer.

Weak reliance

The chatbot says:

“You may want to check your employment contract or speak to HR.”

The employee ignores the qualification and makes an important decision.

Here, reasonable reliance may be much harder to prove.

12. What makes reliance "reasonable"?

Courts may consider:

1. Official appearance

Was it located on the employer's official HR portal?

2. Employer branding

Did it use the employer's name/logo?

3. Language

Did it say:

“According to your HR policy…”

rather than:

“This is general information”?

4. Specificity

Was the chatbot giving a precise answer concerning the employee's individual rights?

5. Employee's knowledge

Would an ordinary employee have reason to suspect the answer was wrong?

6. Availability of alternatives

Was a human HR representative easily accessible?

7. Warning

Was there a clear and prominent warning against reliance?

8. Organisational encouragement

Did the employer tell employees:

“Use our HR chatbot for all employment queries”?

The more the employer encourages reliance, the stronger the argument that reliance was foreseeable.

13. Reliance damages

Reliance damages aim to compensate the claimant for loss suffered because they relied upon the inaccurate representation.

Examples include:

Financial loss

• lost salary;
• unnecessary expenses;
• lost benefits;
• incorrect payroll deductions;
• relocation expenses;
• lost bonus;
• additional tax or financial liability.

Employment opportunity loss

For example:

An HR chatbot incorrectly tells an employee:

“You have an eight-week notice period.”

The employee believes resignation would delay their new job.

They reject the new employment opportunity.

The employee may potentially argue that the chatbot caused economic loss.

Procedural loss

An HR chatbot incorrectly tells an employee:

“You have 90 days to file a grievance.”

The actual deadline is 30 days.

The employee waits and loses the opportunity to pursue the internal procedure.

The legal consequences could potentially be much more serious.

14. Difference between reliance damages and expectation damages

This distinction is important.

Reliance damages

Put the claimant approximately in the position they would have been in had they not relied upon the incorrect information.

Expectation damages

Attempt to put the claimant in the position they would have occupied if the representation had been true.

For example:

An employee relies on a chatbot saying:

“You will receive a £10,000 retention bonus.”

If the employee performs additional work expecting the bonus, the claim may raise questions about whether the loss is:

• expenditure caused by reliance;
• lost opportunity;
• contractual entitlement; or
• merely an expectation of a benefit.

The precise measure depends upon the cause of action and applicable jurisdiction.

15. Causation problem in HR chatbot cases

The employee must generally connect the chatbot's statement to the loss.

Consider:

Chatbot error → employee relies → employee acts → financial loss

For example:

Wrong chatbot advice
↓
Employee believes resignation is subject to 2 weeks' notice
↓
Employee accepts new job starting after 2 weeks
↓
Employer says actual notice is 8 weeks
↓
Employee loses new job
↓
Employee claims damages

The employee must demonstrate that the chatbot's statement materially caused the loss.

16. The "employee should have checked" defence

An employer may argue:

“The employee should have checked the employment contract.”

This can be important.

But it is not automatically decisive.

The strength of this defence depends on circumstances.

For example, if the chatbot says:

“This is only general information. Always check your contract.”

the employer has a stronger argument.

But if the chatbot says:

“I have reviewed your employment information. Your contractual notice period is two weeks.”

the employee has a stronger argument for reasonable reliance.

This reasoning is consistent with Moffatt, where the tribunal rejected the idea that the consumer should simply have checked another part of the company's website when the chatbot itself was an official company information channel.

17. Employer liability versus chatbot liability

A chatbot normally does not become the legal equivalent of an employee merely because it communicates with users.

The more important question is:

Who designed, deployed, controlled and presented the chatbot?

Potentially responsible parties could include:

Employer

For deploying the system and presenting its outputs as HR information.

HR department

Where it failed to maintain accurate policies or appropriate human oversight.

Technology provider

Potentially, depending upon its contract, representations, negligence and applicable law.

Individual HR employee

Potentially where the employee knowingly or negligently configures or approves harmful information.

However, the exact allocation depends heavily on the applicable jurisdiction and contractual arrangements.

18. Special risk in automated HR decision-making

The danger becomes greater when a chatbot does not merely provide information but actually makes or recommends employment decisions.

For example:

“Your attendance score is below the disciplinary threshold. You should receive a final warning.”

or:

“You are not eligible for promotion.”

or:

“Your performance indicates that termination is appropriate.”

This moves beyond simple chatbot information and into automated decision-making.

Potential claims can involve:

• negligence;
• breach of contract;
• discrimination;
• procedural unfairness;
• employment legislation;
• privacy/data protection;
• breach of statutory rights; and
• wrongful dismissal/unfair dismissal, depending on jurisdiction.

19. When liability becomes particularly strong

An HR chatbot negligence claim becomes stronger where several factors coexist:

20. When liability may be weaker

The employer may have stronger defences where:

• the chatbot clearly states that it is not authoritative;
• the employee is instructed to consult HR;
• the employee's contract clearly contradicts the chatbot;
• the employee knew the information was uncertain;
• the employee did not actually rely upon the answer;
• the loss was caused by an independent event;
• the claimed loss is too remote;
• the chatbot merely provides links rather than definitive advice; or
• a valid contractual/statutory limitation applies.

However, these are not automatic defences.

21. Employer's duty to supervise the chatbot

A prudent employer should treat an HR chatbot more like an HR information system than an ordinary conversational tool.

Reasonable safeguards can include:

Accuracy controls

The chatbot should retrieve information from current:

• employment policies;
• contracts;
• HR manuals;
• collective agreements;
• statutory guidance.

Human escalation

High-risk questions should automatically go to HR.

Examples:

• dismissal;
• disciplinary action;
• discrimination;
• whistleblowing;
• grievances;
• harassment;
• contractual disputes;
• pay disputes.

Audit trails

The employer should preserve:

• question asked;
• answer given;
• date/time;
• relevant policy version;
• escalation history.

Testing

The organisation should test the chatbot for:

• hallucinations;
• contradictory answers;
• outdated policies;
• discriminatory outputs;
• incorrect calculations.

Continuous monitoring

A chatbot that was accurate six months ago may become inaccurate after:

• policy changes;
• legislative changes;
• software updates;
• changes to collective agreements.

22. HR chatbot disclaimer strategy

A disclaimer can reduce risk but should not be viewed as a complete immunity mechanism.

A better warning would explain:

“This chatbot provides general HR information and may not account for your individual contract or circumstances. It does not replace advice from HR or an employment adviser. For matters concerning dismissal, disciplinary action, grievances, discrimination, pay disputes, contractual rights or legal deadlines, please contact HR directly.”

More importantly, the system should actually escalate high-risk questions rather than simply displaying a disclaimer.

23. Six-plus case law comparison

The Moffatt decision is particularly significant because it demonstrates that an organisation may be responsible for information generated through its chatbot rather than being able to treat the chatbot as an independent legal actor.

24. Practical example in employment law

Scenario

A company has an HR chatbot called “AskHR.”

An employee asks:

“Can I take 20 days of annual leave next month?”

The chatbot responds:

“Yes. Your manager cannot refuse statutory annual leave.”

The employee books non-refundable travel.

The employer subsequently informs the employee that the requested dates cannot be approved because of operational requirements.

The employee loses £2,000 in cancellation costs.

Possible legal analysis

1. Representation:
The chatbot made a definite statement.

2. Duty:
The employer provided the chatbot as an official HR service.

3. Negligence:
The system supplied an oversimplified or inaccurate representation of the applicable leave policy.

4. Reliance:
The employee booked travel based upon the answer.

5. Reasonableness:
Reliance may be reasonable if the employer encouraged employees to use the chatbot for HR queries.

6. Causation:
The employee would not have incurred the cancellation expense but for the information.

7. Damages:
The employee may seek recovery of legally recoverable loss, subject to applicable employment/tort/contract principles.

25. Important distinction: chatbot error vs legal liability

An inaccurate chatbot answer does not automatically create liability.

There is an important difference:

AI error ≠ automatic negligence

The claimant normally needs to establish the relevant legal elements, particularly:

duty + breach + reasonable reliance + causation + legally recoverable loss.

Therefore, the strongest claims are those where the chatbot:

• gave a clear answer;
• was presented as authoritative;
• concerned an important employment matter;
• was controlled by the employer;
• contained information that should reasonably have been checked;
• was relied upon by the employee; and
• caused foreseeable economic loss.

26. Overall legal position

The emerging principle can be summarised as follows:

An employer generally cannot assume that using AI automatically transfers responsibility for the information supplied by an HR chatbot.

Where an organisation places an HR chatbot within its official systems and employees reasonably rely upon its representations, traditional principles of negligent misstatement, assumption of responsibility, duty of care, causation and economic loss can potentially apply.

Moffatt v Air Canada provides the clearest modern warning: the organisation's responsibility may remain even though the misleading statement came from an automated chatbot.

For HR systems, the legal risk is particularly serious because chatbot errors can affect employment, salary, benefits, disciplinary proceedings, leave, termination and career opportunities. The safest approach is therefore not merely to add an AI disclaimer, but to combine accurate data sources, human escalation, audit logs, continuous testing and clear limits on automated HR advice.

Conclusion

HR chatbot negligence and reliance damages represent an intersection of traditional negligence law and emerging AI governance. The underlying legal principles are not entirely new: courts have long recognised liability for careless statements where a sufficiently close relationship, reasonable reliance and resulting economic loss exist. What AI changes is how the statement is generated and who controls the system.

The most important authorities are Hedley Byrne, Caparo, Esso Petroleum, Spring, Smith v Eric S Bush, Moffatt v Air Canada and Mata v Avianca. Of these, Moffatt is the strongest direct illustration of chatbot liability, while Hedley Byrne, Caparo, Esso and Spring provide the doctrinal foundation for analysing an HR chatbot's duty, reliance and damages.