Information quality (IQ) is vital for successful data-driven activities, especially in the era of Artificial Intelligence (AI). Existing IQ frameworks often overlook the complexities of AI systems, which use diverse and unstructured data. This research proposes a novel IQ framework that integrates emerging dimensions such as provenance, volatility, and trustworthiness, alongside conventional ones like accuracy and completeness. The study offers theoretical and practical insights to help researchers and practitioners ensure high-quality data for AI, enhancing decision-making, fairness, and ethical standards in AI applications.
