Interpretability and accessibility of machine learning in selected food processing, agriculture and health applications

TL;DR

This paper reviews recent advances in making machine learning more interpretable and accessible, focusing on applications in food processing, agriculture, and health to address global challenges and promote ethical AI use.

Contribution

It provides a comprehensive review of interpretability and accessibility techniques in ML across multiple levels and applications in food, agriculture, and health sectors.

Findings

Emerging techniques improve ML interpretability at scientific, statistical, and semantic levels.

Automated model design enhances accessibility of ML systems.

Interpretability is crucial for ethical and regulatory acceptance of AI in critical sectors.

Abstract

Artificial Intelligence (AI) and its data-centric branch of machine learning (ML) have greatly evolved over the last few decades. However, as AI is used increasingly in real world use cases, the importance of the interpretability of and accessibility to AI systems have become major research areas. The lack of interpretability of ML based systems is a major hindrance to widespread adoption of these powerful algorithms. This is due to many reasons including ethical and regulatory concerns, which have resulted in poorer adoption of ML in some areas. The recent past has seen a surge in research on interpretable ML. Generally, designing a ML system requires good domain understanding combined with expert knowledge. New techniques are emerging to improve ML accessibility through automated model design. This paper provides a review of the work done to improve interpretability and accessibility of machine learning in the context of global problems while also being relevant to developing countries. We review work under multiple levels of interpretability including scientific and mathematical interpretation, statistical interpretation and partial semantic interpretation. This review includes applications in three areas, namely food processing, agriculture and health.