Exploring the World of Explainable AI for Tabular Data

Unleashing the Power of Transparency and Interpretability in Machine Learning Models

What is Explainable AI(XAI)?

Machine learning models have become ubiquitous in our daily lives, providing us with conversations, recommendations, predictions, and decision-making support. However, traditional machine learning models operated as a black box, with little transparency or insight into how they arrived at their predictions.

Imagine you and your friend both applying for a loan at the same bank. You have similar incomes and credit scores, but your loan application is rejected while your friend’s application is accepted. The bank does not provide any explanation for why your application was rejected, leaving you feeling frustrated and confused.

As you start to ask around, you hear similar stories from others who have been rejected by the same bank without any clear explanation. This lack of transparency can erode your trust in the bank, as you begin to question the fairness and consistency of their lending practices.

This scenario highlights the importance of trust, transparency, and interpretability in decision-making processes, particularly in critical, high-stakes situations. This is where Explainable AI (XAI) provides methods and techniques to help us understand the underlying rationale of a model’s decision for high-stakes decision-making AI…