Explanatory Paradigm

Explanatory paradigms in machine learning and artificial intelligence research focus on understanding how models arrive at their predictions and decisions, aiming to improve transparency, reliability, and control. Current research emphasizes diverse approaches, including analyzing the interplay of symbolic and connectionist AI within large language models (LLMs), developing frameworks for hybrid human-machine decision-making, and exploring causal reasoning within deep learning architectures. These investigations are crucial for advancing the trustworthiness and practical applicability of AI systems across various domains, from healthcare and manufacturing to climate modeling and resource management.