Explainable and trustworthy artificial intelligence, machine learning, and deep learning

Abstract

The swift progression of artificial intelligence (AI), machine learning (ML), and deep learning (DL) has transformed different industries, offering unprecedented efficiency and innovation. Nevertheless, the growing intricacy and lack of transparency of these technologies have led to important worries about their reliability and ethical consequences. This research explores the growing area of Explainable Artificial Intelligence (XAI) that seeks to improve the clarity, comprehensibility, and responsibility of AI, ML, and deep learning models. XAI helps to increase trust and acceptance by making these technologies easier to understand for users and stakeholders, therefore tackling the "black box" issue. This research provides a thorough examination of the most recent approaches and structures in XAI, with a focus on important strategies like model-agnostic explanations, interpretable models, and post-hoc interpretability techniques. It also examines the important function of XAI in guaranteeing adherence to regulatory standards and ethical guidelines, which are becoming stricter globally. Moreover, the review assesses how XAI is incorporated into different fields such as healthcare, finance, and autonomous systems, illustrating its ability to reduce biases, enhance decision-making, and increase user confidence. This research highlights the significance of XAI in creating AI systems that are both strong and ethical by discussing current trends and developments.

Topics & Concepts