On Explainability in AI-Solutions: A Cross-Domain Survey

TL;DR

This survey reviews the diverse approaches to AI explainability across domains, highlighting the importance of understandable explanations for non-expert users and mapping various methods to their explanatory purposes.

Contribution

It provides a comprehensive overview of existing explainability methods, categorizes reasons for explanations, and discusses the heterogeneity of frameworks in AI.

Findings

Explainability methods are highly heterogeneous across domains.

Different reasons for explanations lead to varied explanatory frameworks.

Understanding AI decisions is crucial for non-expert users.

Abstract

Artificial Intelligence (AI) increasingly shows its potential to outperform predicate logic algorithms and human control alike. In automatically deriving a system model, AI algorithms learn relations in data that are not detectable for humans. This great strength, however, also makes use of AI methods dubious. The more complex a model, the more difficult it is for a human to understand the reasoning for the decisions. As currently, fully automated AI algorithms are sparse, every algorithm has to provide a reasoning for human operators. For data engineers, metrics such as accuracy and sensitivity are sufficient. However, if models are interacting with non-experts, explanations have to be understandable. This work provides an extensive survey of literature on this topic, which, to a large part, consists of other surveys. The findings are mapped to ways of explaining decisions and reasons for explaining decisions. It shows that the heterogeneity of reasons and methods of and for explainability lead to individual explanatory frameworks.