AI Model Errors: Who’s Liable When AI Gets It Wrong?

AI is now making (or influencing) decisions in places that matter: hiring, lending, healthcare, insurance, logistics, customer service, even safety-critical industrial systems. When an AI model makes an error—misdiagnoses a patient, rejects a qualified applicant, flags a legitimate transaction as fraud, or gives dangerous advice—the immediate question is simple:

Who’s responsible?

The legal answer is rarely simple. Liability depends on what went wrong, who controlled the system, what users were told, and whether the harm was foreseeable and preventable.

What counts as an “AI model error”?

“AI error” is often used as a catch-all, but in practice it can mean several different failure modes:

• Incorrect output: The model produces a wrong answer, prediction, or recommendation.

• Hallucinations and fabricated facts: Common with generative AI, where the model outputs plausible but false information.

• Bias and unfair outcomes: The model systematically disadvantages certain groups.

• Data leakage or privacy failures: The model reveals personal data or confidential information.

• Misuse or overreliance: Humans treat the model’s output as authoritative when it was never designed for that.

• Integration failures: The model might be “fine,” but the surrounding software, prompts, retrieval system, or automation causes harm.

This matters because liability often attaches to the cause of the harm—not the fact that “AI was involved.”

The key players: who could be liable?

Most AI deployments involve multiple parties. Any of these can end up in the liability chain.

1) The AI developer (model provider)

This is the organisation that trains the model or provides the core AI system (for example, a foundation model or a proprietary classifier). Potential exposure includes:

• Defective design (unsafe model behaviour)

• Failure to warn about known limitations

• Negligent testing or validation

• Misleading marketing claims (e.g., “accurate,” “safe,” “compliant” without evidence)

However, model providers often argue they are supplying a general-purpose tool and that the deployer controls the real-world use.

2) The deployer (the business using AI)

This is the company that puts the AI into a product or process—often the party closest to the end user. Deployers can be exposed for:

• Negligent selection of a model for a high-risk use

• Poor governance (no monitoring, no human oversight)

• Inadequate staff training

• Failing to implement safeguards (thresholds, escalation, auditing)

• Unfair or unlawful decision-making (especially in employment, credit, and services)

In many cases, the deployer is the most likely target because they have the customer relationship and are easiest to identify.

3) The integrator or vendor (software/consultancy)

If a third party builds the AI feature, integrates it into workflows, or sells an “AI-powered” platform, they may share responsibility—especially if they:

• Designed the end-to-end system

• Configured prompts, guardrails, or decision thresholds

• Recommended the AI for a specific regulated use

• Failed to implement basic controls

4) The data provider

Bad data can create bad outcomes. Data providers can be implicated if they:

• Supplied inaccurate or biased datasets

• Breached privacy or consent rules

• Misrepresented data quality

5) The end user

In some scenarios, user misuse is the main cause. But businesses can’t rely on “user error” if they designed an experience that encourages overreliance.

The legal theories that typically apply

Liability for AI errors usually maps onto existing legal frameworks. Courts and regulators often ask: what duty existed, what standard of care applied, and what harm occurred?

Negligence

Negligence is a common route: did a party fail to take reasonable care?

For AI, “reasonable care” may include:

• Testing the model for the intended use case

• Monitoring performance drift

• Documenting limitations and known failure modes

• Implementing human review where needed

• Providing clear user instructions and warnings

If a business deploys AI in a high-impact context without these controls, negligence claims become more plausible.

Product liability and defective products

In many jurisdictions, product liability can apply if an AI-enabled product is defective and causes harm. Key questions include:

• Is the AI part of a “product” or a “service”?

• Was the product unsafe compared to what a person is entitled to expect?

• Were warnings adequate?

As AI becomes embedded in physical products (vehicles, medical devices, industrial systems), product liability becomes more relevant.

Misrepresentation and consumer protection

If marketing or sales materials overstate what the AI can do—“guaranteed accuracy,” “fully automated compliance,” “no human review needed”—that can trigger claims.

Even B2B buyers may argue they relied on statements about performance, safety, or regulatory readiness.

Contractual liability (B2B)

In commercial deployments, contracts often decide who pays when something goes wrong.

Common contract levers include:

• Warranties (performance, non-infringement, compliance)

• Limitation of liability clauses

• Indemnities (IP infringement, data breaches, regulatory fines)

• Service levels and support obligations

In practice, many disputes are resolved through contract interpretation rather than novel “AI law.”

Data protection and privacy

If AI uses personal data, liability can arise from:

• Unlawful processing

• Lack of transparency

• Inadequate security

• Automated decision-making rules (where applicable)

Privacy liability can hit both developers and deployers depending on who determines the purpose and means of processing.

Discrimination and fairness laws

If AI decisions produce discriminatory outcomes, claims can arise even if no one intended discrimination.

This is especially sensitive in:

• Hiring and HR

• Lending and credit

• Insurance pricing and underwriting

• Housing and tenant screening

A key risk: “We used a third-party model” is rarely a defence if your business made the decision.

Why AI liability is harder than traditional software liability

AI systems behave differently from deterministic software:

• Probabilistic outputs: Models are designed to be “usually right,” not always right.

• Opacity: It can be hard to explain why a model produced a result.

• Emergent behaviour: Generative models can produce unexpected outputs.

• Continuous change: Models drift as data changes; updates can alter behaviour.

These traits don’t remove liability, but they complicate how fault is proven and how “reasonable care” is defined.

A practical way to think about liability: control + foreseeability

A useful rule of thumb is:

• The more control a party has over the system and its deployment, the more likely they are to carry liability.

• The more foreseeable the harm, the more likely a duty existed to prevent it.

For example:

• If a deployer uses AI to automatically deny claims with no human review, foreseeable harm is high.

• If a model provider knows a model hallucinates medical advice but markets it for clinical use, foreseeability is high.

Real-world scenarios (and who may be on the hook)

Scenario A: AI gives incorrect professional advice

A generative AI assistant tells a user to take a dangerous action (financial, legal, medical). Potential liability may involve:

• The deployer, if the product was positioned as professional-grade advice

• The integrator, if they removed safeguards or encouraged reliance

• The developer, if they failed to warn about known risks

Disclaimers help, but they’re not a magic shield—especially if the product experience contradicts the disclaimer.

Scenario B: AI makes a discriminatory decision

A hiring tool screens out candidates from a protected group at a higher rate.

• The employer (deployer) is often the primary target.

• The vendor may share liability if they sold it as “bias-free” or failed to test.

Scenario C: AI causes a safety incident

An AI-powered industrial monitoring system fails to detect a hazard.

• Product liability may apply if the system is part of a product.

• Negligence may apply if monitoring and maintenance were inadequate.

Scenario D: AI leaks personal data

A chatbot reveals customer account details due to poor authentication.

• The deployer is exposed for security failures.

• The vendor/integrator may share responsibility if they designed the flow.

How businesses reduce liability (without killing innovation)

If you’re deploying AI, the goal isn’t “zero risk.” The goal is to show you acted responsibly.

1) Define the use case and risk level

Document:

• What the model is allowed to do

• What it must never do

• Who the user is

• What harm could occur if it fails

High-impact use cases need stronger controls.

2) Put humans in the loop (where it matters)

Human oversight is not a checkbox. It should be designed:

• Clear escalation paths

• Review thresholds

• Audit trails

• Training for reviewers

3) Test for the real world, not the demo

Testing should include:

• Edge cases

• Adversarial prompts (for generative AI)

• Bias and fairness checks

• Performance across different user groups

• Monitoring for drift post-launch

4) Be honest in your marketing and UX

Avoid absolute claims. Use clear language:

• “Assists with” rather than “replaces”

• “May be inaccurate” plus guidance on verification

• Transparent limitations

5) Contract for reality

If you’re buying or selling AI:

• Define responsibilities for monitoring and updates

• Clarify who handles regulatory issues

• Set incident response obligations

• Align indemnities with actual control

6) Keep records

When something goes wrong, documentation matters:

• Model cards and risk assessments

• Testing results

• Change logs

• User reports and incident tickets

Good records can reduce liability and speed up resolution.

What this means for customers and the public

From a user perspective, AI liability often comes down to the organisation that:

• Offered the AI feature

• Benefited commercially from it

• Had the ability to implement safeguards

That’s usually the deployer. But as regulation evolves, model providers and integrators may face increasing obligations too.

The bottom line

When AI gets it wrong, liability rarely sits with “the AI.” It sits with people and organisations—developers, deployers, integrators, and sometimes data providers—based on control, foreseeability, and whether reasonable steps were taken to prevent harm.

If you’re deploying AI, the best defence is not a disclaimer. It’s governance: clear use cases, strong testing, human oversight, transparent communication, and contracts that match how the system is actually used.

Disclaimer: This article is for general informational purposes only and does not constitute legal advice. For advice on a specific situation, consult a qualified legal professional.