The State of Post-Hoc Local XAI Techniques for Image Processing: Challenges and Motivations

TL;DR

This paper reviews post-hoc local XAI techniques for image processing, discussing their motivations, challenges, and open problems to enhance AI transparency and trustworthiness.

Contribution

It provides a comprehensive overview of existing XAI approaches for image processing, highlighting challenges and proposing future research directions.

Findings

Identifies key challenges in post-hoc local XAI methods.

Highlights open problems for improving XAI techniques.

Suggests future research directions for XAI in image processing.

Abstract

As complex AI systems further prove to be an integral part of our lives, a persistent and critical problem is the underlying black-box nature of such products and systems. In pursuit of productivity enhancements, one must not forget the need for various technology to boost the overall trustworthiness of such AI systems. One example, which is studied extensively in this work, is the domain of Explainable Artificial Intelligence (XAI). Research works in this scope are centred around the objective of making AI systems more transparent and interpretable, to further boost reliability and trust in using them. In this work, we discuss the various motivation for XAI and its approaches, the underlying challenges that XAI faces, and some open problems that we believe deserve further efforts to look into. We also provide a brief discussion of various XAI approaches for image processing, and finally discuss some future directions, to hopefully express and motivate the positive development of the XAI research space.