What is algorithmic discrimination?

Algorithmic discrimination occurs when an AI system or automated decision-making tool produces outcomes that disproportionately disadvantage individuals based on race, gender, age, disability, or other protected characteristics. In Massachusetts, these claims may be brought under M.G.L. c. 151B, Chapter 93A, and applicable federal civil rights statutes.

Can I sue an insurance company for using AI to deny my claim?

Yes. When an insurer uses an algorithmic system to deny, delay, or undervalue claims in a manner that produces discriminatory outcomes or constitutes an unfair practice, the insurer may face liability under Massachusetts Chapter 93A, Chapter 176D, and applicable federal law. Recent litigation against major insurers has challenged the use of AI to override clinical recommendations and systematically deny coverage.

Can I sue a company for AI hiring discrimination in Massachusetts?

Yes. Massachusetts law prohibits employment discrimination regardless of whether the discriminatory decision was made by a human or an algorithm. If an AI hiring tool screens out qualified candidates based on proxies for protected characteristics, the employer and the vendor may both face liability under M.G.L. c. 151B and Title VII.

What laws apply to algorithmic discrimination in Massachusetts?

Massachusetts General Laws Chapter 151B prohibits discrimination in employment, housing, credit, and public accommodations. Chapter 93A prohibits unfair or deceptive practices, including algorithmic conduct that misleads or harms consumers. Federal statutes including Title VII, the ADA, the Fair Housing Act, and the Equal Credit Opportunity Act provide additional protections.

How do you prove algorithmic bias in court?

Algorithmic bias may be established through statistical evidence of disparate impact, analysis of training data and model architecture, audit reports or regulatory findings, internal company documents, and expert testimony on algorithmic fairness. Massachusetts courts apply burden-shifting frameworks that require the defendant to justify the business necessity of the system once disparate impact is shown.

What damages are available in algorithmic discrimination cases?

Available damages may include compensatory damages for economic and emotional harm, statutory damages, treble damages under Chapter 93A for willful or knowing violations, injunctive relief requiring changes to algorithmic systems, and attorney fees. The specific remedies depend on the cause of action and the nature of the harm.

Algorithmic Discrimination

Algorithmic discrimination in hiring, lending, insurance, healthcare, or credit may give rise to civil claims under Massachusetts and federal law.

In Mobley v. Workday (N.D. Cal. 2025), a federal court certified the first collective action alleging AI hiring discrimination, with potential class members numbering in the hundreds of millions. 1

Legal Framework

Massachusetts prohibits discrimination in employment, housing, credit, and public accommodations under M.G.L. c. 151B. These protections apply regardless of whether the discriminatory act was performed by a human or an algorithm. When a company deploys an AI system that produces disparate impact on the basis of race, gender, disability, age, or other protected characteristics, the company bears the burden of justifying the business necessity of that system. Chapter 93A independently prohibits unfair or deceptive practices, providing treble damages and mandatory attorney fee-shifting for algorithmic conduct that misleads or harms Massachusetts consumers. Federal statutes including Title VII, the ADA, the Fair Housing Act, and the Equal Credit Opportunity Act reinforce these protections.

Overview

AI-driven discrimination or deception in commerce exposes companies to treble damages and mandatory fee-shifting under Massachusetts Chapter 93A for willful or knowing violations.

The FTC’s enforcement action against Rite Aid established the first federal baseline for algorithmic fairness compliance after the company’s facial recognition system disproportionately flagged minority customers. 2

Types of Algorithmic Discrimination

Claims arise from a growing range of automated systems. AI-driven insurance underwriting and claims-processing tools can systematically deny coverage or benefits. Algorithmic healthcare triage and treatment systems can allocate resources unequally or override clinical judgment. AI hiring and recruitment platforms can screen out qualified candidates using proxies for race, gender, age, or disability. Algorithmic credit and lending systems can deny applications or impose higher rates based on biased training data. Automated ad-delivery platforms can steer opportunities in employment, education, or housing based on protected characteristics.

Proving Algorithmic Bias

Plaintiffs in algorithmic discrimination cases may establish liability through statistical evidence of disparate impact, analysis of training data and model design, audit results or regulatory findings identifying discriminatory outputs, internal documents revealing that the company knew of or disregarded bias, and expert testimony on algorithmic fairness and technical standards. Massachusetts courts apply burden-shifting frameworks that require the defendant to demonstrate business necessity once disparate impact is shown.

Algorithms that discriminate are accountable under the same laws that apply to human decision-makers.

Emerging Regulatory Landscape

Federal and state regulators are increasing enforcement against algorithmic discrimination. Title VII continues to apply to AI-driven hiring decisions, even though the EEOC withdrew its 2023 technical guidance on the subject in January 2025. The Colorado AI Act, amended in May 2026 and now scheduled to take effect January 1, 2027, will require disclosures and transparency around certain automated decision systems. NYC Local Law 144 mandates independent bias audits for automated employment decision tools. The FTC’s enforcement action against Rite Aid for discriminatory facial recognition established the first federal baseline for algorithmic fairness compliance. Massachusetts Attorney General enforcement actions under 93A and 151B may target companies deploying biased AI systems without adequate testing or disclosure.

What to Bring to a Consultation

Relevant materials may include denial letters or adverse action notices from automated systems, communications referencing algorithmic or AI-driven decision-making, records of the application or transaction at issue, evidence of similarly situated individuals who received different outcomes, and any bias audit reports, regulatory filings, or public disclosures by the company. Not all individuals will have documentation. The absence of records does not preclude a viable claim. Many cases rely on statistical analysis, public regulatory filings, and materials obtained through discovery. Algorithmic discrimination cases in Massachusetts frequently involve parallel employment law protections, civil rights theories, Chapter 93A consumer protection claims, and, where personal data is misused, data privacy violations.

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