Towards Explainable Artificial Intelligence

TL;DR

This paper discusses recent advances in explainable AI, emphasizing the importance of transparency in deep learning models, especially in critical fields like medicine, and advocates for broader adoption of explainability methods.

Contribution

It provides an overview of recent developments in explainable AI and advocates for wider practical use of interpretable machine learning techniques.

Findings

Deep learning models often act as 'black boxes' with limited interpretability.

Explainability methods are increasingly developed to visualize and interpret complex models.

The paper calls for broader adoption of explainable AI in practical applications.

Abstract

In recent years, machine learning (ML) has become a key enabling technology for the sciences and industry. Especially through improvements in methodology, the availability of large databases and increased computational power, today's ML algorithms are able to achieve excellent performance (at times even exceeding the human level) on an increasing number of complex tasks. Deep learning models are at the forefront of this development. However, due to their nested non-linear structure, these powerful models have been generally considered "black boxes", not providing any information about what exactly makes them arrive at their predictions. Since in many applications, e.g., in the medical domain, such lack of transparency may be not acceptable, the development of methods for visualizing, explaining and interpreting deep learning models has recently attracted increasing attention. This introductory paper presents recent developments and applications in this field and makes a plea for a wider use of explainable learning algorithms in practice.