1. Introduction

Algorithmic bias in healthcare refers to systematic and unfair differences in the predictions, recommendations, diagnoses, treatment decisions, or allocation of medical resources produced by computer algorithms and artificial intelligence (AI) systems. Such bias may arise from unrepresentative training data, flawed statistical assumptions, discriminatory historical practices, inaccurate proxies for health needs, or inappropriate deployment of a model in a new patient population.

Healthcare algorithms are increasingly used for disease prediction, radiology, pathology, cancer screening, intensive-care triage, insurance assessments, hospital resource allocation, genetic testing, and clinical decision support. Although these technologies can improve efficiency and diagnostic accuracy, biased systems may reproduce or intensify existing inequalities.

A landmark scientific example is the 2019 study by Ziad Obermeyer and colleagues, which demonstrated racial bias in a widely used US healthcare risk-prediction algorithm. The system predicted future healthcare costs as a proxy for health needs, thereby systematically underestimating the needs of Black patients who historically received less healthcare spending than similarly ill White patients.

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Central legal principle: A healthcare algorithm should not be judged solely by its overall accuracy. Its design, data, clinical purpose, differential impact, explainability, and consequences for patients must also be examined.

An important distinction is that algorithmic bias is a technical and social phenomenon, whereas unlawful discrimination is a legal conclusion requiring application of the relevant jurisdiction's laws. A biased outcome can provide evidence of discrimination, but its legal significance depends on the circumstances and applicable legislation.

2. Meaning and principal forms of algorithmic bias

A. Data bias

Training datasets may inadequately represent women, racial or ethnic minorities, people with disabilities, rural communities, children, older people, or patients with uncommon conditions. A system trained primarily on one population may perform poorly on another.

B. Proxy bias

A system uses a measurable variable, such as healthcare expenditure or prior hospital admissions, as a substitute for the actual clinical objective. If access to healthcare differs between groups, the proxy may systematically misrepresent their needs.

C. Representation and measurement bias

Symptoms, diagnostic images, clinical notes, and medical measurements may be recorded differently across patient groups. The resulting data can distort predictions even where the algorithm itself treats every input identically.

D. Deployment bias

A model validated in one hospital, region, or patient population may be used elsewhere without adequate validation. Changes in disease prevalence, clinical practice, equipment, and patient demographics can undermine performance.

E. Automation and feedback bias

Clinicians may defer excessively to algorithmic recommendations. If a biased decision affects who receives treatment, the resulting records may then reinforce the same bias in future training data.

These forms of bias can overlap. For example, underdiagnosis in a particular community may produce incomplete records, which can then train a model to underestimate that community's disease risk.

3. How algorithmic bias creates legal liability

Algorithmic bias can engage several distinct branches of law.

Constitutional and equality law: Where public hospitals or state authorities use discriminatory systems, unequal treatment may violate constitutional equality guarantees.

Medical negligence: A clinician or healthcare institution may be liable for unreasonable reliance on a defective algorithm, failure to validate its use, or failure to respond to evident warning signs.

Product liability: A defective medical AI product may create liability for its manufacturer or other responsible parties under applicable product-safety law.

Data protection and privacy: Improper processing of sensitive medical information, opaque profiling, or unlawful automated decision-making may violate privacy legislation.

Administrative law: A public authority's algorithm-assisted decision may be challenged for arbitrariness, inadequate reasons, improper delegation, or failure to consider relevant circumstances.

Consumer protection and professional regulation: Misleading claims about diagnostic accuracy, inadequate disclosure, or failure to meet professional standards may trigger additional consequences.

The existence of algorithmic bias does not automatically establish every form of liability. Courts generally need to consider the duty owed, applicable legal prohibition, evidence of defective conduct or system design, causation, and the injury suffered.

4. Landmark cases and their legal significance

A critical point for legal research is that there are relatively few reported judgments directly deciding whether a healthcare AI algorithm is unlawfully biased. Several leading authorities instead establish principles of equality, medical negligence, privacy, automated decision-making, or scientific evidence that can be applied to algorithmic healthcare disputes.

Case 1: Obermeyer et al. — Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations (2019)

Science, Vol. 366, pp. 447–453 · Research study, not a court judgment

Facts: Researchers examined a widely used commercial algorithm that identified patients who would benefit from additional care. The algorithm relied on predicted healthcare expenditure as a proxy for medical need.

Finding: At equivalent risk scores, Black patients were sicker than White patients. Because unequal access to healthcare affected expenditure, the model underestimated the needs of many Black patients. The researchers estimated that correcting the proxy could increase the proportion of Black patients selected for additional care from 17.7% to 46.5%.

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Legal significance:

A model can be discriminatory in its effects without explicitly using race as an input.

A seemingly objective statistical proxy can reproduce historical inequality.

Healthcare providers should assess whether the variable being predicted actually corresponds to the clinical need that matters.

Evidence of group disparities may help establish the factual basis of a discrimination, negligence, or regulatory complaint, subject to the applicable legal requirements.

Principle: Predicting healthcare expenditure is not necessarily equivalent to predicting illness or the need for treatment.

Case 2: Griggs v. Duke Power Co., 401 U.S. 424 (1971)

United States Supreme Court · Employment discrimination

Facts: An employer imposed educational and testing requirements that disproportionately excluded Black employees.

Decision: The Supreme Court established a foundational approach to disparate-impact discrimination under Title VII: apparently neutral employment practices can be unlawful when they disproportionately disadvantage a protected group and cannot be justified under the applicable legal standard.

Relevance to healthcare algorithms: Although this was not a medical or AI case, the reasoning provides an analogy for evaluating neutral clinical scoring rules, eligibility criteria, or resource-allocation systems that disproportionately disadvantage a protected group.

For example, a hospital algorithm might use a historical utilization threshold that systematically excludes patients from communities with poor access to specialist care.

Limitation: Griggs concerns employment law. It does not itself establish a general right against algorithmic discrimination in healthcare. A healthcare claimant must identify the applicable statutory or constitutional protection and satisfy its requirements.

Case 3: Washington v. Davis, 426 U.S. 229 (1976)

United States Supreme Court · Equal protection

Facts: Applicants challenged a government employment test because it disproportionately disadvantaged Black applicants.

Decision: The Court held that a disproportionate racial impact, by itself, did not establish a constitutional equal-protection violation in the circumstances of the case; discriminatory purpose was relevant to the constitutional claim.

Relevance to healthcare AI: The case highlights an important distinction between evidence of disparate outcomes and the legal test for proving discrimination. A model may produce unequal outcomes, but the requirements for a successful claim depend on the legal route pursued.

Principle: Statistical disparity can be important evidence, but it does not satisfy every discrimination test automatically.

This distinction matters because a healthcare algorithm may be demonstrably less accurate for one group while the claimant still has to establish the elements of a particular statutory, constitutional, or negligence claim.

Case 4: Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579 (1993)

United States Supreme Court · Scientific and expert evidence

Facts: The litigation concerned expert scientific evidence in a pharmaceutical-products liability action.

Decision: The Court interpreted the federal expert-evidence standard to require trial judges to assess the reliability and relevance of scientific expert testimony.

Relevance to healthcare AI: In a dispute involving a diagnostic algorithm, expert evidence may need to address:

Whether the model was independently validated.

Whether the testing population represented the affected patient.

Whether the reported sensitivity and specificity were appropriate for the clinical use.

Whether subgroup error rates were examined.

Whether the alleged algorithmic defect caused or contributed to the patient's injury.

Principle: A model's technical complexity or a manufacturer's claim of accuracy does not automatically make its output reliable scientific evidence.

Limitation: Daubert governs the admissibility of expert evidence in US federal courts. It is not a ruling that establishes liability for biased medical AI.

Case 5: National Federation of the Blind v. Target Corp., 452 F. Supp. 2d 946 (N.D. Cal. 2006)

United States District Court · Disability accessibility

Facts: The litigation challenged accessibility barriers on a retailer's website under disability-discrimination law.

Decision: The court allowed important aspects of the accessibility claim to proceed, in the context of the relationship between the website and the retailer's physical stores.

Relevance to healthcare AI: Digital healthcare interfaces, automated patient portals, symptom checkers, and AI-assisted appointment systems can create exclusion where people with disabilities cannot access or effectively use them.

Principle: Digital systems should be assessed for their practical accessibility, not simply for whether a service exists in electronic form.

Limitation: This was not an algorithmic-bias case, and its reasoning must be applied cautiously to healthcare systems and under the governing accessibility legislation.

Case 6: State of Punjab v. Mohinder Singh Chawla, (1997) 2 SCC 83

Supreme Court of India · Medical treatment and constitutional obligations

Facts and legal issue: The case concerned medical reimbursement and the State's obligations in relation to healthcare for government employees.

Decision: The Supreme Court recognised the importance of medical treatment within the constitutional understanding of the right to health under Article 21.

Relevance to algorithmic healthcare: Where a public hospital or government health scheme uses automated tools to allocate treatment, deny referrals, or prioritise patients, the use of technology does not remove the public authority's constitutional responsibilities.

A claimant could argue that an arbitrary or inadequately justified algorithm-assisted decision violates applicable constitutional or statutory protections. The argument would require evidence of the decision, its impact, and the relevant legal duty.

Limitation: This judgment did not address AI, statistical bias, or algorithmic discrimination directly. It supplies constitutional healthcare context rather than a direct precedent against biased algorithms.

Case 7: K.S. Puttaswamy v. Union of India, (2017) 10 SCC 1

Supreme Court of India · Constitutional privacy

Facts: The case concerned whether privacy is protected as a fundamental right under the Indian Constitution.

Decision: A nine-judge bench unanimously recognised privacy as a fundamental right, grounded in constitutional guarantees including Articles 14, 19 and 21.

Relevance to healthcare AI: Medical AI may process sensitive information such as diagnoses, genetic information, reproductive health records, disability data, and behavioural information. Unjustified collection or use of such data can raise privacy and autonomy concerns.

The judgment is particularly relevant where an institution uses patient information for model training, secondary purposes, or profiling without an adequate legal basis or appropriate safeguards.

Principle: Medical innovation does not displace constitutional privacy protections.

Limitation: Puttaswamy did not establish a specific legal test for algorithmic bias. Privacy and discrimination are related but distinct issues: a system can protect confidentiality yet still discriminate.

Case 8: Suchita Srivastava v. Chandigarh Administration, (2009) 9 SCC 1

Supreme Court of India · Reproductive autonomy and bodily integrity

Facts: The case concerned the reproductive rights and autonomy of a woman with an intellectual disability.

Decision: The Supreme Court recognised reproductive choice as an aspect of personal liberty under Article 21 and emphasised the importance of consent and bodily integrity.

Relevance to healthcare AI: Automated systems used in reproductive medicine, fertility treatment, disability-related care, or genetic counselling should not replace the legal and ethical requirements governing consent and individual autonomy.

For example, an AI system that recommends denying fertility treatment solely on the basis of a disability-related risk score could raise questions about equality, consent, and lawful clinical decision-making.

Limitation: The case did not concern algorithmic decision-making. It is relevant as a source of principles protecting autonomy and dignity.

5. Statutory framework governing algorithmic bias

A. India

India does not have one comprehensive statute that specifically regulates all forms of algorithmic discrimination in healthcare. Several existing legal frameworks may apply.

The constitutional principles in K.S. Puttaswamy and Suchita Srivastava are particularly relevant to privacy, dignity, autonomy, and informed medical decision-making. However, neither judgment establishes that every use of a biased algorithm is automatically unconstitutional.

B. United States

A particularly relevant regulatory provision is 45 CFR § 92.210, concerning nondiscrimination in patient-care decision-support tools. It prohibits covered entities from discriminating on specified protected grounds through such tools and requires reasonable efforts to identify and mitigate certain discrimination risks.

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Its application depends on the regulation's scope, the entity involved, the relevant protected ground, and the applicable enforcement rules.

Other potentially relevant sources include:

Title VI of the Civil Rights Act of 1964, where its requirements apply to federally funded healthcare programs.

Section 504 of the Rehabilitation Act of 1973, concerning disability discrimination by covered entities.

The Americans with Disabilities Act, where applicable.

Medical-device regulation and state medical-negligence law.

C. European Union

The EU framework combines data protection, anti-discrimination law, medical-device regulation, and the Artificial Intelligence Act.

EU AI Act — Regulation (EU) 2024/1689

The Act establishes risk-based requirements for AI systems. Certain AI systems used in medical devices or other healthcare contexts may be classified as high-risk, depending on their intended purpose and the applicable classification criteria.

Relevant requirements include risk management, data governance, technical documentation, transparency, human oversight, accuracy, and monitoring. The Act also contains provisions concerning bias detection and correction.

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GDPR — Regulation (EU) 2016/679

Relevant provisions include:

Article 5: principles governing personal-data processing.

Article 9: special categories of personal data, including health and genetic data.

Article 22: safeguards concerning certain decisions based solely on automated processing.

Articles 13–15: information and access rights relevant to automated decision-making.

Article 22 does not create a blanket prohibition on AI-assisted medical decisions. Its application depends on the nature and effect of the decision and the relevant statutory conditions.

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The EU AI Act's precise obligations and implementation dates must be checked against the particular provision and any applicable amendments. The Commission's AI Act Service Desk describes a bias-detection provision permitting certain processing of sensitive personal data under strict necessity, security, access-control, and retention safeguards.

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6. Hypothetical healthcare dispute: how a court might analyse algorithmic bias

Consider a public hospital that introduces an AI system to identify patients requiring urgent cardiac treatment.

The system has been trained mainly on data from urban hospitals. It gives lower risk scores to rural patients whose symptoms are recorded differently and who have less complete medical histories. Several rural patients consequently receive delayed referrals.

The legal analysis would proceed through the following stages.

Identify the adverse decision. Determine whether the algorithm caused a lower triage score, denied treatment, delayed referral, or otherwise affected the patient.

Establish the disparity. Obtain appropriate evidence comparing error rates, false negatives, and treatment outcomes across relevant patient groups.

Investigate the cause. Examine training data, input variables, missing records, validation procedures, deployment conditions, and clinical thresholds.

Identify the legal duty. Determine whether the hospital is subject to constitutional equality obligations, statutory nondiscrimination rules, professional duties, privacy requirements, or medical-device regulation.

Establish causation and injury. Assess whether the algorithm's failure materially contributed to delayed diagnosis, worsened health, additional expenses, or another legally recognised injury.

Determine the remedy. Depending on the jurisdiction and evidence, relief could include reconsideration of the decision, correction of the system, regulatory action, compensation, or other appropriate judicial orders.

An important evidentiary issue is access to technical information. Hospitals and vendors may possess the model documentation, validation results, logs, and subgroup-performance data needed to investigate the claim. Applicable procedural and disclosure rules determine how this evidence can be obtained.

7. Legal responsibilities of hospitals, clinicians, and AI developers

Responsibility may be shared. A developer might create a poorly validated model, while a hospital might deploy it outside its intended population. A clinician might also disregard symptoms that clearly contradict the model's output. The legal outcome depends on each party's duties, conduct, and causal contribution.

8. Measures for preventing algorithmic bias

A sound compliance programme should include:

Representative data: Evaluate whether the training and validation datasets adequately cover the intended patient population.

Subgroup testing: Measure sensitivity, specificity, false-positive rates, and false-negative rates across relevant groups.

Clinical validation: Test performance in the actual clinical environment before relying on the system.

Independent auditing: Use appropriately independent reviewers to examine disparities and document corrective actions.

Meaningful human oversight: Ensure clinicians can question, override, and escalate algorithmic recommendations.

Patient transparency: Explain the role of AI in significant clinical decisions in a manner appropriate to the circumstances and legal requirements.

Continuous monitoring: Reassess performance after changes to populations, equipment, clinical practice, or software.

Incident reporting: Record errors, complaints, adverse outcomes, and corrective measures.

Privacy-protective fairness testing: Use demographic and health data for auditing only through appropriate lawful mechanisms and safeguards.

There is no single fairness metric suitable for every healthcare application. Equal false-negative rates, equal sensitivity, and equal calibration can conflict, especially where disease prevalence differs. The appropriate assessment must therefore reflect the clinical purpose, patient safety, legal obligations, and consequences of errors.

9. Critical legal challenges

Three difficulties are especially significant.

First, proving discrimination. Patients may have evidence of unequal outcomes without access to the technical information needed to show why the algorithm produced them. The applicable legal standard determines whether disparity, intent, statistical evidence, or another form of proof is required.

Second, allocating responsibility. Healthcare algorithms are often developed by one company, purchased by another organisation, and used by individual clinicians. Contracts do not necessarily determine the rights of injured patients or eliminate statutory duties.

Third, balancing privacy and fairness. Detecting racial, sex-based, disability-related, or other disparities may require demographic information. Yet collecting and processing that information can itself raise privacy and discrimination concerns. The solution is appropriately controlled auditing, not the assumption that either privacy or fairness can simply be ignored.

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10. Conclusion

Algorithmic bias in healthcare is a legal issue involving equality, medical negligence, privacy, scientific evidence, patient autonomy, and regulatory accountability.

The research in Obermeyer et al. demonstrates why apparently neutral predictions can perpetuate unequal healthcare access. Griggs and Washington v. Davis illustrate how the law distinguishes discriminatory impact from the requirements of particular discrimination claims. In India, Puttaswamy, Suchita Srivastava, and Mohinder Singh Chawla provide relevant constitutional context, although they are not direct judgments on AI bias.

The central lesson is that algorithmic assistance does not eliminate human or institutional accountability. Healthcare organisations must evaluate whether an AI system is clinically valid for its intended population, monitor unequal outcomes, protect patient information, and respond appropriately when the system produces harmful decisions.

For India, the most persuasive legal analysis will ordinarily combine the facts of the patient's case with applicable constitutional guarantees, data-protection law, medical-device requirements, consumer law where applicable, and established medical-negligence principles. A claim should not rely on the existence of bias alone; it should connect the evidence of bias to a specific legal duty, breach, and remedy.

Research note: The scientific study discussed above is not a court judgment. The other cases are genuine judicial authorities, but they largely establish general principles rather than directly adjudicating biased healthcare AI. This distinction is essential when citing them in academic work or legal proceedings.