Explainable artificial intelligence (XAI) is concerned with the development of techniques that can enhance the interpretability, accountability, and transparency of artificial intelligence and, in particular, machine learning algorithms and models, especially black-box ones, such as artificial neural networks, so that these can also be adopted in areas, like healthcare, where the interpretability and understanding of the results (e.g. classifications) are required.
Which XAI techniques are there?
If there are many, to avoid making this question too broad, you can just provide a few examples (the most famous or effective ones), and, for people interested in more techniques and details, you can also provide one or more references/surveys/books that go into the details of XAI. The idea of this question is that people could easily find one technique that they could study to understand what XAI really is or how it can be approached.