The 6 Benefits of Explainable AI (XAI) | Improve accuracy, decrease harm and tell better stories

Explainable AI (XAI), also known as interpretable machine learning (IML), can help you understand and explain your model. This has many benefits. It can help decrease harm and increase trust in machine learning. You can also gain knowledge of your dataset and tell better stories about your results. You can even improve the accuracy of your models and performance in production. We will discuss these 6 benefits in depth. We then end by touching on the limitations of XAI.
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XAI: • Explainable AI (XAI)
SHAP: • SHAP
Algorithm fairness: • Algorithm Fairness
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🚀 Chapters 🚀
00:00 Introduction
01:24 Approaches in XAI
02:22 Benefit 1: improve accuracy
03:26 Benefit 2: debugging models
05:09 Benefit 3: decrease harm
07:21 Benefit 4: build trust
10:21 Benefit 5: telling stories
12:27 Benefit 6: gain knowledge
13:54 Limitations of XAI

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XAI course: adataodyssey.com/courses/xai-with-python/
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