A systematic review of Explainable Artificial Intelligence models and applications: Recent developments and future trends

Abstract

Artificial Intelligence (AI) uses systems and machines to simulate human intelligence and solve common real-world problems. Machine learning and deep learning are Artificial intelligence technologies that use algorithms to predict outcomes more accurately without relying on human intervention. However, the opaque black box model and cumulative model complexity can be used to achieve. Explainable Artificial Intelligence (XAI) is a term that refers to Artificial Intelligence (AI) that can provide explanations for their decision or predictions to human users. XAI aims to increase the transparency, trustworthiness and accountability of AI system, especially when they are used for high-stakes application such as healthcare, finance or security. This paper offers systematic literature review of XAI approaches with different application and observes 91 recently published articles describing XAI development and applications in healthcare, manufacturing, transportation, and finance. We investigated the Scopus, Web of Science, IEEE Xplore and PubMed databases, to find the pertinent publications published between January 2018 to October 2022. It contains the published research on XAI modelling that were retrieved from scholarly databases using pertinent keyword searches. We think that our systematic review extends to the literature on XAI by working as a roadmap for further research in the field.

Ähnliche Arbeiten