Trustworthy XAI and Application

TL;DR

This paper discusses the importance of Trustworthy Explainable AI (XAI), focusing on transparency, explainability, and trustworthiness, and reviews recent applications across various fields to promote reliable and ethical AI deployment.

Contribution

It provides a comprehensive overview of XAI components and reviews recent studies demonstrating their application in real-world scenarios.

Findings

XAI enhances trust and accountability in AI systems.

Recent studies show successful application of XAI in diverse fields.

Understanding XAI components is crucial for ethical AI deployment.

Abstract

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.