Building an Explainable AI Solution from Scratch
In this chapter, we will use the knowledge and tools we acquired in the previous chapters to build an explainable AI (XAI) solution from scratch using Python, TensorFlow, Facets, and Google's What-If Tool (WIT).
We often isolate ourselves from reality when experimenting with machine learning (ML) algorithms. We take the ready-to-use online datasets, use the algorithms suggested by a given cloud AI platform, and display the results as we saw in a tutorial we found on the web. Once it works, we move on to another ML algorithm and continue like this, thinking that we are learning enough to face real-life projects.
However, by only focusing on what we think is the technical aspect, we miss a lot of critical moral, ethical, legal, and advanced technical issues. In this chapter, we will enter the real world of AI with its long list of XAI issues.
Developing AI code in the 2010s relied on knowledge and talent. Developing AI...