Explainability of Algorithms

TL;DR

This paper discusses the nature of algorithm opaqueness, its ethical implications, and reviews various explainability methods in AI, highlighting ongoing challenges in making complex models transparent and understandable.

Contribution

It provides a comprehensive analysis of different types of algorithm opaqueness and evaluates current explainability techniques in AI.

Findings

Opaqueness arises from technical complexity and proprietary secrecy.

Explainability methods are still facing significant challenges.

Understanding opacity is crucial for ethical AI development.

Abstract

The opaqueness of many complex machine learning algorithms is often mentioned as one of the main obstacles to the ethical development of artificial intelligence (AI). But what does it mean for an algorithm to be opaque? Highly complex algorithms such as artificial neural networks process enormous volumes of data in parallel along multiple hidden layers of interconnected nodes, rendering their inner workings epistemically inaccessible to any human being, including their designers and developers; they are "black boxes" for all their stakeholders. But opaqueness is not always the inevitable result of technical complexity. Sometimes, the way an algorithm works is intentionally hidden from view for proprietary reasons, especially in commercial automated decision systems, creating an entirely different type of opaqueness. In the first part of the chapter, we will examine these two ways of understanding opacity and the ethical implications that stem from each of them. In the second part, we explore the different explanatory methods that have been developed in computer science to overcome an AI system's technical opaqueness. As the analysis shows, explainable AI (XAI) still faces numerous challenges.