Challenges in XAI
Keywords: XAI
Abstract: This essay delves into the dual challenges faced in the realm of Artificial Intelligence (AI): balancing the performance of AI models with their explainability, and establishing standardization in the explanation techniques and presentation used in AI systems. The burgeoning complexity of AI, especially in deep learning, has led to the "black box" problem, where the inner workings of these models are often opaque to users. This poses significant challenges, particularly in critical sectors such as healthcare, finance, and legal systems, where understanding AI decisions is as crucial as the decisions themselves. The essay examines recent advancements in AI, highlighting studies on Large Language Models (LLMs) and deep convolutional neural networks, which demonstrate significant progress in developing interpretable AI systems. Additionally, it addresses the necessity of standardizing AI explanations, focusing on both the technical derivation of explanations and their translation into user-friendly formats. This exploration is critical for ensuring responsible and accessible use of AI across various domains.
Submission Number: 217
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