The Role of Metadata in Promoting Explainability and Interoperability of AI-based Prediction Models

Abstract

This systematic literature review delves into the critical role of metadata in enhancing the explainability and interoperability of AI-based prediction models. As AI technologies permeate various sectors, the need for transparent and coherent predictive models becomes increasingly vital. Metadata, which refers to structured information that contextualizes data, serves as a pivotal component in achieving this transparency and coherence. This review synthesizes 50 existing literature to evaluate how different types of metadata—such as descriptive, structural, and administrative metadata—contribute to the understanding and integration of AI models, thereby facilitating better decision-making and trust in AI systems. The review identifies key themes related to the challenges of explainability, including the complexity of AI algorithms and the opacity of model outputs, and discusses how robust metadata frameworks can mitigate these issues by providing essential context and clarity about model decisions. Furthermore, it examines the significance of interoperability in AI applications, highlighting how standardized metadata can enable seamless integration across diverse systems and platforms. The findings underscore the necessity of developing comprehensive metadata strategies to enhance both the interpretability of AI predictions and the compatibility of AI systems across different environments. Ultimately, the review calls for continued research into metadata standards and practices that can promote a more reliable and user-friendly AI landscape.