Governance Frameworks for Ensuring Algorithmic Decision Data Integrity in AI Systems and Regulatory Compliance
Pelumi Damola Adeyinka
*
Obafemi Awolowo University, Ile-Ife, Osun State, Nigeria.
Emonena Patrick Obrik-Uloho
Prairie View A&M University, 100 University Dr, Prairie View, TX 77446, United States.
Olufunke Cynthia Metibemu
Ekiti State University, Ado-Ekiti, Nigeria, Iworoko Road, PMB 5363, Ado-Ekiti, Ekiti State, Nigeria.
Cornelia Ifeoma Ejoh
University of the District of Columbia, 4200 Connecticut Ave NW, Washington, DC 20008, United States.
Christopher Ugbong Akeke
Howard University, 2400 Sixth Street NW, Washington, DC 20059-0001, United States.
*Author to whom correspondence should be addressed.
Abstract
Artificial intelligence systems increasingly mediate consequential decisions in credit allocation, healthcare triage, employment screening and public administration, yet the data underpinning these decisions is frequently incomplete, mislabelled, stale or quietly altered as it moves through long and opaque pipelines. This review examines governance frameworks intended to preserve the integrity of algorithmic decision data and to align organisational practice with an increasingly dense regulatory landscape spanning the European Union, the United States and international standard-setting bodies. It synthesises literature on data quality theory, documentation artefacts such as datasheets and model cards, blockchain-based provenance mechanisms, algorithmic auditing regimes and sector-specific compliance obligations in finance and healthcare. The review finds that technical solutions for data quality monitoring have matured considerably faster than the institutional arrangements needed to make such monitoring auditable, contestable and legally enforceable, producing a persistent gap between what is technically feasible and what is organisationally practised. It further finds that regulatory instruments, notably the General Data Protection Regulation and the Artificial Intelligence Act, converge on transparency and documentation obligations but diverge on enforcement mechanics, creating compliance friction for organisations operating across jurisdictions. The review proposes a layered governance model integrating data-level controls, documentation practices, human oversight and independent auditing, and identifies future research priorities around interoperable provenance standards, cross-border regulatory harmonisation and the measurement of data integrity as a continuous rather than a point-in-time property.
Keywords: Algorithmic governance, data integrity, AI regulatory compliance, data provenance, algorithmic accountability, artificial intelligence audit.