Towards Transparent Artificial Intelligence: Exploring Modern Approaches and Future Directions

Abstract

This paper delves into recent advancements in artificial intelligence (AI) interpretability, highlighting the increasing necessity for transparency in complex AI systems. We examine three key approaches: Kolmogorov-Arnold Networks (KANs), which introduce a novel neural network design; Dictionary Learning and Sparse Autoencoders for interpretable feature extraction; and various Explainable AI (XAI) techniques. The study underscores the challenges posed by black-box models and the importance of integrating both qualitative and quantitative insights in AI decision-making. We discuss the trade-offs between model performance and interpretability and examine how these methodologies can enhance trust, accountability, and safety in AI applications. The paper concludes by identifying current challenges and future research directions in AI interpretability, emphasizing the need for scalable, robust, and ethically sound approaches as AI systems continue to evolve and impact diverse domains.