The landscape of AI vendor liability is undergoing a fundamental shift, creating an uncomfortable position for businesses deploying AI systems. Federal courts are pioneering legal theories that hold AI vendors directly accountable for discriminatory outcomes, while vendor contracts become more aggressive in shifting liability to customers. The result is a “liability squeeze” leaving businesses responsible for AI failures that they cannot fully audit, control, or understand.
The Mobley Precedent: Vendors as Legal Agents
The Mobley v. Workday case fundamentally altered AI liability frameworks. In July 2024, Judge Rita Lin allowed the discrimination lawsuit to proceed against Workday as an “agent” of companies using its automated screening tools. This marked the first time a federal court applied agency theory to hold an AI vendor directly liable for discriminatory hiring decisions.
The legal reasoning is both straightforward and profound in its implications. When AI systems perform functions that are traditionally handled by employees—such as screening job applicants—the vendor has been “delegated responsibility” for that function. Under this theory, Workday wasn’t merely providing software; it was acting as the employer’s agent in making hiring decisions.
The case achieved nationwide class action certification in May 2025, covering all applicants over the age of 40 rejected by Workday’s AI screening system. Derek Mobley’s experience illustrates the exponential nature of AI discrimination: he applied to over 100 jobs through Workday’s system and was rejected within minutes each time. Unlike individual human bias, a single biased algorithm can multiply discrimination across hundreds of employers and thousands of applicants.
Contract Risk-Shifting Acceleration
While courts expand vendor liability, the contracting landscape tells a different story.
Market analysis from legal tech platforms reveals systematic risk-shifting patterns in vendor contracts. A recent study found that 88% of AI vendors impose liability caps on themselves, often limiting damages to monthly subscription fees. In addition, only 17% provide warranties for regulatory compliance, a significant departure from standard SaaS practices. And broad indemnification clauses routinely require customers to hold vendors harmless for discriminatory and other outcomes.
This creates dynamics where vendors develop and deploy AI systems knowing legal responsibility will ultimately rest with customers. Businesses using biased algorithms may find themselves sued for discrimination while discovering their vendor contracts prevent recourse for underlying defects.
The Practical Impact
Consider a mid-sized retailer using AI-powered applicant tracking. Under the Mobley precedent, both the retailer and AI vendor could face discrimination claims. However, the vendor’s contract likely contains:
- Liability caps limiting damages (often to annual fees or a modest multiplier);
- No or limited compliance warranties regarding fair hiring practices;
- Broad indemnification requiring the retailer to defend the vendor against discrimination claims; and
- Limited audit rights preventing algorithmic examination for bias.
The retailer, therefore, becomes legally responsible for discriminatory outcomes caused by the algorithms it cannot examine, using the training data it cannot audit, with decision-making logic it cannot fully understand. This represents a fundamental breakdown in the traditional relationship between risk and control.
Strict Liability Development
The liability squeeze may intensify as legal scholars and courts explore strict product liability theories for advanced AI systems, particularly “agentic” AI capable of autonomous multi-step tasks. Unlike negligence requiring proof of unreasonable conduct, strict liability focuses on whether products were defective and caused harm.
For AI systems that can autonomously enter contracts, make financial decisions, or take actions on behalf of users, a single “hallucination” or erroneous decision could constitute not just performance failure but product defect with potentially unlimited liability—but only if vendor contracts don’t successfully shield them from liability.
Strategic Response Framework
Aggressive Contract Negotiation
Legal teams must approach AI vendor negotiations as primary risk management exercises rather than standard procurement. Key provisions include:
- Mutual liability caps rather than one-sided vendor protection;
- Explicit compliance warranties for applicable regulations;
- Audit rights allowing examination of algorithmic decision-making;
- Indemnification for discrimination and bias claims caused by the AI tool; and
- Performance guarantees with measurable metrics.
Insurance Strategy Evolution
Traditional insurance policies also create coverage gaps for AI-related liabilities. Discrimination claims from biased algorithms don’t easily fit into cyber, errors & omissions, or general liability coverage. Organizations, therefore, should:
- Conduct comprehensive coverage gap analyses for AI deployments;
- Negotiate specific AI coverage endorsements;
- Consider emerging specialized AI insurance products; and
- Document governance practices to support coverage claims.
Internal Governance as Legal Defense
Robust internal AI governance is becoming the primary legal defense against discrimination claims, including:
- Bias testing protocols with documented methodologies and results;
- Regular algorithmic audits by qualified third parties;
- Decision-making transparency with clear human oversight (“human-in-the-loop”) procedures;
- Vendor due diligence processes evaluating bias mitigation practices; and
- Impact monitoring systems tracking outcomes across protected classes.
Looking Ahead
The AI vendor liability landscape evolves in opposite directions simultaneously. Courts expand accountability while contracts limit it. This divergence creates immediate practical risks for businesses deploying AI systems.
The most dangerous assumption is that vendor liability shields will hold. As the Mobley case demonstrates, legal theories evolve faster than contract terms. Businesses relying solely on vendor liability caps may find themselves holding full responsibility for algorithmic failures they could not control or predict.
The solution isn’t avoiding AI; it’s approaching AI deployment with understanding that ultimate liability increasingly rests with deployers. This reality demands rigorous vendor due diligence, thoughtful contract negotiation, and comprehensive internal governance.
In the age of algorithmic decision-making, careful attention to liability allocation isn’t paranoia, it’s prudent risk management for an evolving legal landscape where responsibility and control no longer align as traditional business models assumed.