Get more out of Explainable AI (XAI): 10 Tips

Explainable Artificial intelligence (XAI), also known as Interpretable Machine Learning (IML), can explain complex machine learning models. But, the methods are not a golden bullet. You can’t simply fire them at black-box models and expect reasonable explanations for their inner workings. Yet, they can still provide incredible insight if used correctly.
So, I give 10 tips for getting the most out of XAI methods. The tips are roughly divided into 3 groups. The first four tips focus on the underlying data used to train models. The next four focus on you as a user of XAI methods. The last two delve into more technical considerations for XAI Python packages.
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🚀 Useful playlists 🚀
XAI: • Explainable AI (XAI)
SHAP: • SHAP
Algorithm fairness: • Algorithm Fairness
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🚀 Chapters 🚀
00:00 Introduction
01:36 Tip1: Incorporate domain knowledge
03:08 Tip 2: Build intuitive features
04:16 Tip 3: Reduce correlated features
05:55 Tip 4: start with a simple model
06:52 Tip 5: Understand your audience
07:53 Tip 6: Avoid conclusions about causality
08:52 Tip 7: Be aware of confirmation bias
10:12 Tip 8: Use multiple methods
11:03 Tip 9: Use TreeShap
11:58 Tip 10: Python package dependencies

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