Algorithmic Gatekeepers: Genetic Discrimination, AI-Inferred Risk, and the ELSI of Algorithmic Access to Healthcare, Insurance, and Employment

AI-enabled genomic and health-risk prediction systems are increasingly being developed for precision medicine, population health, clinical decision support, insurance analytics, workplace health programs, and other settings where predictive models shape access to services, resources, and opportunities. These systems are becoming algorithmic gatekeepers. They may influence who receives genomic testing, who is prioritized for prevention or precision therapies, how risk is classified by insurers, and how health-related information is used in employment or workplace settings.These developments offer important benefits. Genomic AI may support earlier diagnosis, better prevention, more targeted care, and more efficient allocation of clinical and institutional resources. At the same time, the use of predictive models in high-stakes settings raises difficult questions about fairness, validation, transparency, accountability, discrimination, and public trust. These questions are especially pressing when algorithmic systems use genetic or genomic data directly, or when they infer genetic predispositions, ancestry-linked risk, familial vulnerability, disability-related traits, or future health status from other forms of data.This Research Topic examines the responsible development, evaluation, deployment, and governance of AI systems that use, combine, or infer genomic and health-related risk information. As clinical sequencing, population biobanks, consumer genomics, electronic health records, wearable devices, and large-scale biomedical datasets become more interconnected, genetic information may no longer appear only as a disclosed test result. It may also be inferred from complex combinations of genomic, clinical, behavioral, environmental, and demographic data. This shift creates new challenges for model validation, population representativeness, bias assessment, privacy-preserving analytics, explainability, and the governance of inferred genetic or health-related risk.Regulatory and policy frameworks are beginning to respond, but important gaps remain. In Europe, the EU AI Act, GDPR, and emerging health-data initiatives place increasing emphasis on high-risk AI, data protection, risk management, transparency, and fundamental rights. In China, rules on personal information protection, algorithmic recommendation, generative AI, scientific data, and health-data governance are shaping the development and deployment of biomedical AI. Across Asia and other regions, national strategies for precision medicine, digital health, AI regulation, and population-scale genomic infrastructure are creating new opportunities as well as new governance challenges. It remains unclear how these frameworks should apply when AI systems generate or use genomic and health-risk predictions that affect eligibility, prioritization, pricing, employment screening, or access to clinical services.This Research Topic aims to bring together researchers from genomics, biomedical AI, bioinformatics, medicine, public health, law, bioethics, social science, insurance studies, labor governance, and AI policy. We particularly encourage submissions from European and Asian perspectives, including China and other jurisdictions developing large-scale genomic, digital health, or AI governance infrastructures. We also welcome interdisciplinary work from teams embedded in clinical genomics, biobank, precision medicine, public health, or AI-health initiatives, especially where technical research is linked to responsible innovation, fairness, trustworthiness, and the governance of predictive health data.To advance discussion of the scientific, ethical, legal, regulatory, and social boundaries of genomic AI and algorithmic risk prediction, we welcome contributions addressing, but not limited to, the following themes:* Clinical validation and performance assessment of genomic AI and health-risk prediction systems* Population diversity, ancestry representation, and bias in AI-enabled genomic medicine* Algorithmic fairness, bias auditing, explainability, and accountability in genomic and biomedical AI* Privacy-preserving analytics, federated learning, and data protection for genomic AI* Patient, clinician, public, insurer, or employer attitudes toward AI-mediated genomic risk prediction* Cross-border health-data governance, biobank governance, and population-scale genomic infrastructures* Comparative governance of genomic AI in Europe, China, Asia, and other jurisdictions* Insurance analytics, actuarial classification, and the boundaries of permissible health-risk prediction* Workplace wellness programs, wearable-derived inference, and employment-related health profiling* Legal and ethical analysis of genetic discrimination, inferred genetic risk, and algorithmic non-discrimination* Case studies of AI-mediated access to genomic testing, prevention, precision therapies, or clinical services* Governance models for trustworthy genomic AI, responsible innovation, and fair institutional decision-making* Technical and institutional approaches to auditing proxy discrimination based on ancestry, family history, disability, race, ethnicity, or health status* Public health uses of genomic AI, including prevention, screening, stratification, and population-level risk governance* The role of national precision medicine programs, biobanks, and health-data infrastructures in shaping fair access to genomic AI* Regulatory treatment of inferred genetic information under AI, data protection, employment, insurance, and health law* Comparative studies of how different health systems govern AI-mediated access to genomic testing, prevention, care, or coverage* Ethical, legal, and technical strategies for contesting or correcting harmful algorithmic classifications* Responsible innovation frameworks for integrating genomic AI into clinical, insurance, workplace, and public health settings Topic editors: Dov Greenbaum Source: https://www.frontiersin.org/research-topics/82649/algorithmic-gatekeepers-genetic-discrimination-ai-inferred-risk-and-the-elsi-of-algorithmic-access-to-healthcare-insurance-and-employment