There are a variety of methods or approaches for ensuring transparency and comprehensibility when it comes to artificial intelligence. We have summarized the most important ones for you below:
Layer-wise Relevance Propagation (LRP) was first described in 2015. This is a technique for determining which characteristics of inputs contribute the most to the output of a neural network.
The counterfactual method describes how data inputs (e.g. text, images, diagrams, etc.) are specifically changed after receiving the results. Then, one observes how much the output has changed as a result.
Local Interpretable Model-Agnostic Explanations (LIME) is an explanatory model with a holistic approach. It is designed to explain any machine learning classifier and resulting prediction. This enables users from other fields to understand the data and procedures.
Rationalization is a procedure that is used specifically for AI-based robots. It involves designing a machine so that it can explain its actions autonomously.