Conceptualizing Responsible AI Governance Structures for Automated Decision Systems in Regulated Industries
Abstract
Automated decision systems are increasingly embedded in the core operations of regulated industries such as banking, insurance, healthcare, and utilities, where their outputs can affect creditworthiness, medical treatment, employment, and access to essential services. This paper conceptualizes a governance structure for responsible artificial intelligence (AI) that regulated organizations can adopt to manage the risks associated with ADS while preserving their capacity to innovate. Drawing on the regulatory landscape that had taken shape by 2023, including the European Union's proposed AI Act, the United States National Institute of Standards and Technology AI Risk Management Framework, sector-specific supervisory guidance in banking and insurance, and long-standing model risk management practice, the paper proposes a seven- layer governance architecture spanning board oversight, cross-functional coordination, model validation, technical assurance, compliance mapping, independent audit, and stakeholder redress. Each layer is examined in detail, and a worked scenario illustrates the structure applied to a consumer credit model across its full lifecycle. The framework is then applied to six regulated sectors, including energy and telecommunications alongside financial services, healthcare, insurance, and employment, to illustrate how the same structural logic adapts to different statutory obligations and risk profiles. The paper closes with a discussion of practical tensions and cross- cutting themes, recommendations with an implementation roadmap, and directions for future research.
References
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