Integrated Data and AI Governance Framework: A Lifecycle Approach to Responsible AI Implementation

Abstract

Data governance and artificial intelligence governance have come together as a necessity when organizations want to introduce responsible AI systems to scale. The given article proposes an end-to-end data and artificial intelligence (AI) governance framework that envisions data governance and AI ethics in the context of the AI lifecycle and the important interplay between data integrity and model ethics. The offered structure contains four main steps, including data source and preparation, model development, deployment, operations, and feedback and iteration with embedded governance checkpoints and automated controls. With its ability to create a coherent framework on top of which business organizations can execute and implement the mechanisms of building AI systems that balance performance and ethical alignment, the framework proposed allows companies to integrate AI systems that operate on a global scale. A framework checklist associated with essential principles, such as data quality, lineage, and compliance, and AI-specific elements of fairness, transparency, accountability, and robustness are covered in the article. Using role-based accountability roles, automated systems of compliance, and governance orchestration platforms, organizations should be able to operationalize responsible AI practices without reducing innovation velocity. The framework responds to emerging challenges because of the generative versions of AI, federated learning, and cross-border data flows, and deals with changing regulatory environments. Responsible AI must require the cultivation of an effective organizational culture to ensure sustainability in implementation processes, which involves extensive training sessions, top management participation, and performance measures that incorporate the relevant aspects of governance responsibility.