Explainable AI: The Future of Machine Learning

Table of contents

• What is Explainable AI?
• Why is Explainable AI Important?
• Techniques for Explainable AI
• Challenges and Future Directions

The code used in this post is available bellow:

Portfolio/xai/intro.ipynb at main · smortezah/Portfolio

What is Explainable AI?

Machine learning (ML) is a subfield of artificial intelligence that involves the use of algorithms and statistical models to enable computer systems to learn from data and make predictions or decisions without being explicitly programmed. However, as ML models become more complex, they can become increasingly difficult to interpret. This lack of interpretability can lead to ethical concerns, such as biased decision-making and lack of transparency. For example, an ML model used in healthcare may make a diagnosis based on a set of features, but it may not be clear why the model made that diagnosis or how it arrived at that decision.