Trustworthy XAI and Application
This is an incremental review article discussing XAI's importance for accountability and fairness in AI systems, relevant for researchers and practitioners.
The paper addresses the problem of AI's 'black box' nature by examining Explainable Artificial Intelligence (XAI) to enhance trust through transparency, explainability, and trustworthiness, reviewing its applications across various fields.
Artificial Intelligence (AI) is an important part of our everyday lives. We use it in self-driving cars and smartphone assistants. People often call it a "black box" because its complex systems, especially deep neural networks, are hard to understand. This complexity raises concerns about accountability, bias, and fairness, even though AI can be quite accurate. Explainable Artificial Intelligence (XAI) is important for building trust. It helps ensure that AI systems work reliably and ethically. This article looks at XAI and its three main parts: transparency, explainability, and trustworthiness. We will discuss why these components matter in real-life situations. We will also review recent studies that show how XAI is used in different fields. Ultimately, gaining trust in AI systems is crucial for their successful use in society.