Classification
Legal and Regulatory Frameworks
Overview
Sectoral U.S. rules refer to the approach where artificial intelligence (AI) systems are governed under pre-existing regulatory frameworks specific to various economic sectors, rather than through a unified, comprehensive AI law. For example, the Food and Drug Administration (FDA) oversees AI in medical devices, the Securities and Exchange Commission (SEC) and Federal Trade Commission (FTC) regulate AI in financial services, and the Equal Employment Opportunity Commission (EEOC) addresses AI in employment contexts. This approach leverages domain expertise and tailored compliance standards but can result in regulatory gaps, inconsistencies, and challenges for organizations deploying cross-sector AI systems. Furthermore, as AI capabilities rapidly evolve, sectoral regulators may struggle to keep pace, leading to uncertainty or insufficient oversight in emerging applications. A limitation of this model is its potential to leave some uses of AI unregulated or subject to conflicting requirements, particularly for technologies that cut across traditional sectoral boundaries.
Governance Context
Within the U.S., sectoral rules for AI governance impose concrete obligations such as: (1) FDA's Software as a Medical Device (SaMD) guidelines, requiring premarket review and post-market surveillance for AI-driven health tools; (2) SEC's Regulation SCI, mandating operational resilience and risk controls for automated trading systems; (3) EEOC's enforcement of Title VII of the Civil Rights Act, requiring employers to ensure AI hiring tools do not result in disparate impact discrimination. These obligations are grounded in statutory mandates and enforced through audits, reporting requirements, and penalties. Additionally, organizations must often conduct algorithmic impact assessments or provide transparency reports to demonstrate compliance. However, sectoral regulators often issue guidance rather than binding rules for AI, creating a patchwork of controls that may not fully address AI-specific risks such as algorithmic bias or explainability. Coordination among agencies, as seen in joint statements or interagency task forces, is an emerging but still limited practice.
Ethical & Societal Implications
Sectoral rules can leave significant ethical gaps, as not all AI applications fit neatly within existing regulatory silos. This may result in inconsistent protections for individuals, especially in areas like privacy, fairness, and transparency. Vulnerable populations may be disproportionately affected if sectoral regulators lack the expertise or authority to address novel AI harms. Additionally, the absence of uniform standards can complicate accountability and public trust, particularly when AI systems operate across multiple domains or jurisdictions. The lack of comprehensive oversight can also slow the identification and remediation of systemic bias, exacerbating existing social inequalities.
Key Takeaways
Sectoral U.S. rules leverage existing regulatory expertise but create a fragmented AI governance landscape.; Major sectoral agencies include the FDA (healthcare), SEC/FTC (finance), and EEOC (employment).; Obligations often focus on safety, fairness, and transparency, but may not address all AI-specific risks.; Cross-sector AI deployments can face conflicting or insufficient regulatory requirements.; Ethical, societal, and legal gaps persist, especially for emerging or multi-sector AI applications.; Coordination among sectoral agencies is limited but increasingly recognized as necessary.; Sectoral rules may lag behind rapid AI advancements, challenging effective oversight.