Towards Benchmarking Explainable Artificial Intelligence Methods

TL;DR

This paper critically examines the capabilities and limitations of current explainability methods for neural networks, emphasizing the importance of benchmarking to develop trustworthy AI systems.

Contribution

It introduces a philosophical framework for evaluating explainability methods and presents a case study highlighting their dependence on human knowledge and reasoning.

Findings

Explainability methods require human domain knowledge to be effective.

Current methods are ambiguous and insufficient for building trust or making actionable decisions.

Benchmarking is essential for advancing trustworthy AI.

Abstract

The currently dominating artificial intelligence and machine learning technology, neural networks, builds on inductive statistical learning. Neural networks of today are information processing systems void of understanding and reasoning capabilities, consequently, they cannot explain promoted decisions in a humanly valid form. In this work, we revisit and use fundamental philosophy of science theories as an analytical lens with the goal of revealing, what can be expected, and more importantly, not expected, from methods that aim to explain decisions promoted by a neural network. By conducting a case study we investigate a selection of explainability method's performance over two mundane domains, animals and headgear. Through our study, we lay bare that the usefulness of these methods relies on human domain knowledge and our ability to understand, generalise and reason. The explainability methods can be useful when the goal is to gain further insights into a trained neural network's strengths and weaknesses. If our aim instead is to use these explainability methods to promote actionable decisions or build trust in ML-models they need to be less ambiguous than they are today. In this work, we conclude from our study, that benchmarking explainability methods, is a central quest towards trustworthy artificial intelligence and machine learning.