Read more about the research, solutions and education on our blog: Tools for Explainable, Fair and Responsible ML
In this post, we will take a closer look at some algorithms used in explainable artificial intelligence. You will find here an introduction to methods of global and local model evaluation. Each description will include a technical introduction, example analysis, and code in R and Python.
We have prepared an overview of the most popular R-packages, which can be used to build interpretable models or to explore complex ones. Examples of knitr notebooks for more than 30 packages are available at http://xai-tools.drwhy.ai/.
There are various adversarial attacks on machine learning models; hence, ways of defending, e.g. by using Explainable AI methods. Nowadays, attacks on model explanations come to light, so does the defense to such adversary. Here, we introduce fundamental concepts related to the domain. A further reference list is available at https://github.com/hbaniecki/adversarial-explainable-ai.