Explainable AI (XAI) refers to the development of artificial intelligence (AI) systems that can provide transparent and understandable explanations for their decisions and actions. XAI aims to address the “black box” problem of traditional AI systems, where the internal processes and decision-making of the system are not easily understandable or explainable to humans. XAI can enhance the trustworthiness, accountability, and reliability of AI systems, particularly in domains where the consequences of AI decisions can have significant ethical, social, or legal implications. XAI techniques include model interpretability, natural language processing, and interactive visualizations, among others. Ongoing research and development in XAI is needed to improve the accuracy and usefulness of explanations provided by AI systems and to ensure that XAI is implemented ethically and effectively in practice.
- Techniques for Explainable AI
- Challenges in Explainable AI
- Scope of Explainable AI