Decoding Machine Learning: Transparency Matters

Abstract

Machine learning models are often complex and difficult to understand, which can make it difficult for users to trust
them and ensure that they are fair. This is especially important as machine learning is increasingly being used in realworld
applications that have a significant impact on people's lives. One way to address the problem of transparency in
machine learning is to use explainable and interpretable models. These models can provide users with insights into how
the model operates and justifies its predictions to users. This can help users to build trust in the model and to identify
any potential biases or unfairness.