Explainable AI the Latest Advancements and New Trends

TL;DR

This paper surveys recent developments in trustworthy and explainable AI, highlighting new trends and the connection between explainability and meta-reasoning in autonomous systems.

Contribution

It provides a comprehensive survey of global efforts on ethical AI, reviews state-of-the-art interpretability techniques, and discusses emerging trends linking explainability with meta-reasoning.

Findings

Identified key ethical elements for trustworthy AI

Reviewed advanced interpretability techniques

Highlighted the link between explainability and meta-reasoning

Abstract

In recent years, Artificial Intelligence technology has excelled in various applications across all domains and fields. However, the various algorithms in neural networks make it difficult to understand the reasons behind decisions. For this reason, trustworthy AI techniques have started gaining popularity. The concept of trustworthiness is cross-disciplinary; it must meet societal standards and principles, and technology is used to fulfill these requirements. In this paper, we first surveyed developments from various countries and regions on the ethical elements that make AI algorithms trustworthy; and then focused our survey on the state of the art research into the interpretability of AI. We have conducted an intensive survey on technologies and techniques used in making AI explainable. Finally, we identified new trends in achieving explainable AI. In particular, we elaborate on the strong link between the explainability of AI and the meta-reasoning of autonomous systems. The concept of meta-reasoning is 'reason the reasoning', which coincides with the intention and goal of explainable Al. The integration of the approaches could pave the way for future interpretable AI systems.