--- title: "Algorithmic Bias and Fairness, Definition | MedTech Terms" description: "Systematic differences in model performance across demographic or clinical subgroups. Plain-English Software & AI definition for MedTech teams, with examples an" lang: en json-ld: | { "@context": "https://schema.org", "@graph": [ { "@type": "DefinedTerm", "@id": "https://medtechterms.com/terms/ai-bias-fairness#term", "name": "Algorithmic Bias and Fairness", "description": "Algorithmic bias occurs when a model performs differently across subgroups (e.g., by sex, race, age, comorbidity) due to training data imbalance, label noise, or proxy variables. 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[Home](/) 2. / 3. [Terms](/terms) 4. / 5. [Software & AI](/terms?cat=Software%20%26%20AI) 6. / 7. Algorithmic Bias and Fairness [All terms](/terms) Software & AI [AI / ML in Devices](/ecosystems/ai-ml)[Software Lifecycle](/ecosystems/software-lifecycle)[Clinical Evidence](/ecosystems/clinical-evidence) # Algorithmic Bias and Fairness Systematic differences in model performance across demographic or clinical subgroups. Reviewed by [Christian Espinosa, Founder, Blue Goat Cyber](/authors/christian-espinosa) Last reviewed May 5, 2026 ## Definition Algorithmic bias occurs when a model performs differently across subgroups (e.g., by sex, race, age, comorbidity) due to training data imbalance, label noise, or proxy variables. Fairness analyses quantify and mitigate these gaps. What the regulation says Regulators emphasize fair and unbiased performance for AI/ML-based MedTech. The FDA ## What this means in practice FDA's [GMLP](/terms/good-machine-learning-practice) principles call for representative datasets and subgroup performance analysis. [EU AI Act](/terms/eu-ai-act) high-risk requirements explicitly address bias monitoring across the lifecycle. ## Examples - A diagnostic AI trained predominantly on data from one ethnic group performs poorly when used on patients from other ethnic groups, leading to misdiagnoses. - A risk stratification algorithm for disease progression overestimates risk for one sex due to proxy variables correlated with socioeconomic status in the training data. - A wearable device's algorithm, designed to detect a medical condition, shows lower accuracy for individuals with darker skin tones because the training images were not diverse enough. Common pitfalls - • Failing to define fairness metrics relevant to the specific clinical context and patient population. - • Overlooking the potential for bias to be introduced or amplified at various stages of the AI/ML lifecycle, from data acquisition to deployment. - • Assuming that a globally accurate model is also fair across all demographic subgroups. ## Frequently asked questions What is the primary cause of algorithmic bias in MedTech? Algorithmic bias most commonly stems from unrepresentative or imbalanced training datasets, where certain patient subgroups are underrepresented or poorly labeled. How do regulatory bodies like the FDA address algorithmic fairness? Can bias be completely eliminated from AI/ML algorithms? ## Cross-references ### Part of A larger framework or document this term belongs to. - [ Good Machine Learning Practice(GMLP) ](/terms/good-machine-learning-practice) ## Related terms Grouped by theme ### Editor's picks · Hand-selected related concepts [ Software & AI AI/ML-Enabled Medical Device Medical device that uses artificial intelligence or machine learning to perform its intended use. ](/terms/ai-ml-device)[ Software & AI EU AI Act EU regulation establishing risk-based requirements for AI systems, including most medical AI. ](/terms/eu-ai-act)[ Software & AI Good Machine Learning Practice(GMLP) Guiding principles for the development of AI/ML-enabled medical devices. ](/terms/good-machine-learning-practice) ### More in Software & AI · Same category [ Software & AI AAMI TIR45(TIR45) AAMI Technical Information Report providing guidance on applying Agile software development practices within an IEC 62304-compliant medical device software lifecycle. ](/terms/aami-tir45)[ Software & AI Adversarial Robustness Resilience of an ML model to inputs deliberately crafted to cause misclassification. ](/terms/adversarial-robustness)[ Software & AI Algorithm Change Protocol(ACP) The detailed procedural section of a PCCP that specifies how planned modifications to an AI/ML model will be developed, validated, and implemented. ](/terms/algorithm-change-protocol)[ Software & AI Clinical Decision Support(CDS) Software providing healthcare professionals with knowledge and patient-specific information. ](/terms/cds) Cited by Where this term appears across MedTech Terms. Ecosystems (3) - [AI / ML in Devices](/ecosystems/ai-ml) - [Software Lifecycle](/ecosystems/software-lifecycle) - [Clinical Evidence](/ecosystems/clinical-evidence) ## Primary references 3 sources Link health: 3 verified · last checked 2026-06-20 FDA/Health Canada/MHRA· 1 IMDRF· 1 MDCG· 1 1. [1 Good Machine Learning Practice Verified FDA/Health Canada/MHRA · fda.gov ](https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles) 2. [2 IMDRF - Software as a Medical Device Verified IMDRF · imdrf.org ](https://www.imdrf.org/working-groups/software-medical-device-samd) 3. [3 MDCG Software Guidance Verified MDCG · health.ec.europa.eu ](https://health.ec.europa.eu/medical-devices-sector/new-regulations/guidance-mdcg-endorsed-documents-and-other-guidance_en) Inline markers like \[1\] jump to the matching reference above. Sponsor note ### Building software-as-a-medical-device? We help SaMD and AI/ML device teams meet IEC 62304 and the FDA's evolving expectations for software cybersecurity. [Book a 30-minute discovery session](https://go.bluegoatcyber.com/meetings/blue-goat-cyber/discovery-session) - No obligation - Expert-led from minute one - NDA available on request MedTech Terms is a community resource sponsored by [Blue Goat Cyber](https://bluegoatcyber.com). Definitions are independent of any vendor. On this term Category Software & AI Sources 3 Updated 5/5/2026 [Compare with another term](/compare?a=ai-bias-fairness) Learn in 60 seconds Card Lesson Quiz Systematic differences in model performance across demographic or clinical subgroups. - · FDA's GMLP principles call for representative datasets and subgroup performance analysis. - · EU AI Act high-risk requirements explicitly address bias monitoring across the lifecycle. - · Fairness analyses quantify and mitigate these gaps. Remember this Watch out: Failing to define fairness metrics relevant to the specific clinical context and patient population. Related terms - [Good Machine Learning Practice(GMLP) ](/terms/good-machine-learning-practice) - [AI/ML-Enabled Medical Device ](/terms/ai-ml-device) - [EU AI Act ](/terms/eu-ai-act) You may also need Auto-suggested from Software & AI and shared keywords. - [Model Card ](/terms/model-card) - [Synthetic Data ](/terms/synthetic-data) - [IVDR Performance Evaluation ](/terms/ivdr-performance-evaluation) - [Algorithm Change Protocol(ACP) ](/terms/algorithm-change-protocol) - [Data Governance (AI/ML) ](/terms/data-governance) - [Federated Learning ](/terms/federated-learning) [All Software & AI terms](/terms?cat=Software%20%26%20AI) From the Blue Goat network Related resources and services on this topic. - [ AI SaMD Insights How AI is changing SaMD - risk, regulation, and good machine-learning practice. ](https://ai-samd.com) - [ Med Device Cyber Podcast Conversations with medical device cybersecurity practitioners. ](https://mdcpodcast.com) MedTech Terms An authoritative, plain-language reference for the regulatory, quality, cybersecurity, and software terms that shape modern medical devices. 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