Who’s Liable When AI Gets It Wrong? Understanding Legal Responsibility in the Age of Artificial Intelligence

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Artificial Intelligence (AI) is everywhere and is powering chatbots, approving loans, diagnosing illnesses, and even making hiring recommendations. These systems promise efficiency and accuracy, but what happens when AI gets it wrong? From wrongful arrests due to faulty facial recognition, to biased hiring decisions, to misdiagnoses in healthcare, AI errors can have serious legal and financial consequences. The pressing question is who’s liable? In the U.S. and globally, the law is still catching up. Liability for AI errors can depend on factors such as: (1) the role of human oversight; (2) whether the harm was foreseeable; (3) the contractual relationships between parties; and (4) applicable statutes, case law, and regulatory guidance. This article explores product liability, professional liability, data protection laws, and contractual risk allocation to help businesses and professionals understand who’s responsible when AI makes a mistake.

Why AI Mistakes Are Different from Human Errors

AI is not a “person” under the law. It cannot be sued, fined, or jailed. So, the responsibility flows to humans and organizations but pinpointing which party is responsible can be complicated for obvious reasons.

Key differences:

  1. Opacity (“Black Box” Problem): AI decision-making may not be transparent, making it harder to prove fault.
  2. Autonomy: Some AI systems operate with minimal human intervention, raising questions about control.
  3. Scale of Impact: An AI flaw can affect thousands or millions of people simultaneously.

Potentially Liable Parties When AI Fails

  1. AI Developers and Vendors
  • Legal theory: Product liability, negligence, breach of warranty
  • Example: If a self-driving car’s navigation AI malfunctions due to flawed coding, the developer may be liable under product liability law.
  • Challenge: Many AI systems are “services” rather than “products,” which can complicate product liability claims.
  1. Deploying Businesses
  • Legal theory: Negligence, vicarious liability, breach of statutory duty
  • Example: An employer uses an AI hiring tool that systematically rejects older applicants. Even if the bias is in the software, the employer may face liability under Title VII of the Civil Rights Act or Age Discrimination in Employment Act (ADEA).
  • Why? Courts often hold that the party making the final decision (or benefiting from it) is responsible, regardless of AI origin.
  1. Professionals Using AI in Decision-Making
  • Legal theory: Professional malpractice
  • Example: A doctor relies solely on AI diagnostic software and misses a critical illness. Even if the AI was wrong, the physician may be liable for failing to apply professional judgment.
  1. Data Providers
  • Legal theory: Negligent misrepresentation, breach of contract, violation of privacy laws
  • Example: An AI lender denies applications based on flawed credit data supplied by a third party. The data supplier could share liability.
  1. End Users
  • Legal theory: Misuse of technology, breach of contract, unlawful reliance
  • Example: A small business owner ignores AI warnings in financial software and causes a tax reporting error. Liability may fall on the user.

Legal Theories for AI Liability

A. Negligence

Occurs when a party fails to exercise reasonable care in developing, deploying, or supervising AI systems.

Key question: Did the party take reasonable steps to prevent foreseeable harm from AI errors?

B. Product Liability

Traditional product liability law holds manufacturers and sellers responsible for defective products. This applies if:

  • AI is classified as a “product”
  • The defect is in design, manufacturing, or warning/instructions

Note: Some courts have yet to clarify whether cloud-based AI counts as a “product” for strict liability purposes.

C. Breach of Contract

AI vendors often limit liability through contract terms, but if the AI fails to meet agreed performance standards, customers may sue for breach.

D. Statutory Liability

AI mistakes can trigger violations of specific statutes such as:

  • Fair Credit Reporting Act (FCRA): inaccurate credit decisions
  • Equal Credit Opportunity Act (ECOA): discriminatory lending
  • Americans with Disabilities Act (ADA): inaccessible services

Real-World Examples of AI Gone Wrong

  • Facial Recognition & Wrongful Arrests: Several U.S. cities have faced lawsuits after police used faulty facial recognition software leading to false arrests.
  • Apple Card Gender Bias Allegations: New York investigated claims that women received lower credit limits than men, despite similar credit histories.
  • Self-Driving Car Accidents: Tesla and other autonomous vehicle companies have faced lawsuits when their AI-powered systems allegedly caused crashes.

These cases show that liability often rests with the human or corporate entity deploying AI even when the root cause is algorithmic.

What Are the International Perspectives?

European Union

The proposed EU AI Act and AI Liability Directive aim to create clear rules:

  • Higher compliance obligations for “high-risk” AI systems
  • Easier for consumers to prove harm from AI

United Kingdom

The UK has not adopted AI-specific liability laws yet, but regulators have issued guidance on algorithmic accountability under existing discrimination, consumer protection, and data laws.

How to Limit Liability When Using AI

  1. Conduct AI Risk Assessments

Before deploying AI, evaluate potential risks, especially in high-stakes areas like healthcare, hiring, and finance.

  1. Maintain Human Oversight

AI should assist, not replace, human judgment in critical decisions. This is both a legal safeguard and a trust-building measure.

  1. Vet AI Vendors Carefully

Review vendor bias testing reports, accuracy claims, and compliance certifications. It is usually recommended to include indemnification clauses in contracts.

  1. Keep Detailed Documentation

Maintain logs of AI decisions, data sources, and testing procedures. This can help in court and regulatory investigations.

  1. Ensure Regulatory Compliance

Follow sector-specific rules (e.g., FCRA, HIPAA, ECOA) and privacy laws [e.g., California Consumer Privacy Act (CCPA), General Data Protection Regulation (GDPR)].

Future of AI Liability Law

Legal frameworks are evolving to address AI’s unique challenges. In the U.S., courts are currently stretching existing negligence, product liability, and anti-discrimination laws to fit AI. However, specialized AI legislation — similar to the EU’s approach — may be on the horizon. Until then, businesses must operate under the assumption that existing laws apply to AI as if it were human decision-making. If your AI tool discriminates, misleads, or causes harm, expect the same legal consequences as if a human made the mistake.

Conclusion: Liability Starts with Human Responsibility

In conclusion, when AI gets it wrong, someone is accountable and it won’t be the AI. Liability may fall on developers, vendors, deploying businesses, professionals, data providers, or end users depending on the facts. The safest strategy? Treat AI as a tool you are fully responsible for, not an independent decision-maker. Organizations that prioritize transparency, bias mitigation, human oversight, and legal compliance will be best positioned to harness AI’s benefits while minimizing legal exposure. If your business uses AI in high-impact areas, our legal team can help you to assess your AI liability risks, draft contracts that allocate responsibility fairly, develop AI governance and compliance programs, and respond to government regulatory claims.