Beyond Neural Networks – Exploring the Next Frontier in AI Architectures

Abstract

The growing complexity and opacity of modern AI models, particularly deep neural networks, have sparked significant interest in the field of Explainable Artificial Intelligence (XAI). While deep learning has yielded remarkable success across various domains, its lack of interpretability poses critical challenges in transparency, accountability, and trustworthiness. This paper delves into the emerging frontier of AI architectures that prioritize explainability by design, going beyond traditional neural networks. We investigate alternative models such as symbolic AI, neurosymbolic systems, causal inference models, and other hybrid approaches that bridge the gap between performance and transparency. The paper also evaluates the philosophical, ethical, and technical implications of explainability in AI systems and proposes a roadmap for the development of next-generation interpretable AI frameworks.