Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach

Understand

The recent enthusiasm for artificial intelligence (AI) is due principally to advances in deep learning.

• Deep learning methods are remarkably accurate, but also opaque, which limits their potential use in safety-critical applications.
• To achieve trust and accountability, designers and operators of machine learning algorithms must be able to explain the inner workings, the results and the causes of failures of algorithms to users, regulators, and citizens.
• The originality of this paper is to combine technical, legal and economic aspects of explainability to develop a framework for defining the "right" level of explain-ability in a given context.