An IIT Jodhpur researcher who works in the cutting-edge field of Artificial Engineering, analysed the problem of explainability of black-box machine learning models. The research shows how the current philosophy of explainable machine learning suffers from certain limitations that have led to a proliferation of black-box models. Machine learning is one of the most sought-after areas of study today. In that context, the explainability of machine learning models represents a fundamental problem. The main aim of this research was to develop more transparent (explainable) machine learning models that can be deployed in several practical applications where intelligent prediction and analysis are required.

The main goal of the LIME package is to explain any black-box machine learning models. It is used for both classification and regression problems. Let's try to understand why we need to explain machine learning predictions. Consider you are working for a housing finance or bank client. You are tasked with building a machine learning model to predict loan defaults.

This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Last November, Apple ran into trouble after customers pointed out on Twitter that its credit card service was discriminating against women. David Heinemeir Hansson, the creator of Ruby on Rails, called Apple Card a sexist program. "Apple's black box algorithm thinks I deserve 20x the credit limit [my wife] does," he tweeted. The @AppleCard is such a fucking sexist program.

In February 2019 Polish government added an amendment to a banking law that gives a customer a right to receive an explanation in case of a negative credit decision. This means that a bank needs to be able to explain why the loan wasn't granted if the decision process was automatic.