Speakers
Description
As artificial intelligence (AI) systems increasingly penetrate various domains, the need to understand their decision-making processes becomes paramount. This study presents a comprehensive review of Python-based eXplainable AI (XAI) packages aimed at deciphering the black box nature of AI models. By analyzing and comparing prominent XAI techniques and tools, this review sheds light on their functionalities, strengths, and limitations. Through a structured evaluation, we highlight the diverse methods employed by these packages to elucidate AI model behaviors, ranging from feature importance analysis to model-agnostic interpretability methods. Furthermore, we discuss the practical implications of these XAI techniques in enhancing model transparency, fairness, and trustworthiness in real-world applications. By synthesizing key insights from various XAI approaches, this review provides researchers, practitioners, and developers with valuable guidance in selecting appropriate XAI tools for their specific needs, thereby fostering greater transparency and interpretability in AI systems.
