The week's pick
Reseach Article
RAISE: A Responsible AI Implementation Framework through System Ethics for Governing Automated Decision-Making in Business Operations
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 187 - Number 137 |
| Year of Publication: 2026 |
| Authors: Prince Nweke Onyeka, Chinonso Job, Festus Chijioke Onwe |
10.5120/ijcab17c376d5ef4
|
Prince Nweke Onyeka, Chinonso Job, Festus Chijioke Onwe . RAISE: A Responsible AI Implementation Framework through System Ethics for Governing Automated Decision-Making in Business Operations. International Journal of Computer Applications. 187, 137 ( Aug 2026), 48-53. DOI=10.5120/ijcab17c376d5ef4
Abstract
A companion critical review identifies four structural governance gaps in the deployment of AI-driven Automated Decision-Making (AI-ADM) systems across business operations: the absence of systematic algorithmic auditing mechanisms, inadequate reskilling infrastructure for automation-displaced workers, the lack of standardised environmental lifecycle accounting for AI systems, and the complexity of cross-jurisdictional regulatory compliance. This paper proposes RAISE (Responsible AI Implementation through System Ethics), a five-pillar governance framework designed to address these gaps in an integrated and practically deployable manner. The five pillars are: (R) ethics-by-design, embedding ethical requirements into AI-ADM development from inception; (A) AI Ethics Governance Committees, providing multi-disciplinary oversight throughout the AI lifecycle; (I) human-in-the-loop (HITL) systems, maintaining meaningful human oversight in high-stakes decision paths; (S) Green AI practice, minimising the environmental footprint of AI operations; and (E) explainability and accountability mechanisms, ensuring interpretable, auditable, and contestable ADM outputs. RAISE is presented as a design-science artefact following Hevner et al.’s design-science research methodology: each pillar is grounded in published evidence and professional standards, the framework is specified at sufficient operational detail to support organisational implementation, and an evaluation protocol is provided for empirical testing. Feasibility constraints—including cost and resource implications, technical complexity, and organisational change management requirements—are assessed candidly for each pillar. The framework addresses the documented gap between high-level AI ethics principles, which are extensively published but weakly operationalised, and concrete, organisation-level governance practice.
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