TranspareGov-AI: A Multi-Stakeholder Framework for Auditable Algorithmic Decision-Making in Business Processes

The integration of Artificial Intelligence (AI) into core business processes promises significant efficiencies and innovations, but also introduces challenges related to transparency, accountability, and fairness. Effective AI governance is crucial to mitigate risks and build trust among stakeholders. This paper introduces TranspareGov-AI, a novel multi-stakeholder framework designed to enable transparent and auditable algorithmic decision-making within organizational business processes. TranspareGov-AI establishes a structured approach encompassing AI system registration, impact assessment, continuous monitoring with auditable logging (extending concepts like EBBL), stakeholder engagement portals, and independent audit protocols. We detail the framework's architecture, its key components including a Governance Dashboard and Audit Trail Repository, and an implementation within a simulated customer relationship management (CRM) process involving AI-driven lead scoring. Our evaluation demonstrates how TranspareGov-AI facilitates stakeholder understanding, supports compliance with regulatory requirements, and enhances the auditability of AI-driven decisions. The framework provides a practical pathway for organizations to implement responsible AI governance, fostering trust and ensuring alignment with ethical and business objectives.