While the previous chapter covered Human-in-the-Loop and its importance in building responsible AI algorithms, it’s paramount to ensure the transparency and explainability of ML models following HITL processes. This chapter reviews “explainability,” also known as XAI and its implementation.
With the steady advancements of AI in the past few years and decades, we’ve seen a gradual progression from logical, knowledge-based approaches to algorithms made of neural networks. Introduced by two researchers from the University of Chicago, ...
