Unveiling the Surprising Benefits of Explainable Machine Learning: Why Ignoring It Could Be Costly
Why should we focus on Explainable Machine Learning?? Model debugging, Model monitoring, and Model Transparency for Explainable ML
When I first started learning Machine Learning (ML) four or five years ago, the environment was that you were set to go if you knew how to implement domain-specific supervised and unsupervised approaches. As things evolve, so do the expectations from data scientists (or machine learning engineers). Because the field of data science is so dynamic, this is a prerequisite. The challenge now is to build models that are not only reasonable in performance but also explainable.
All the efforts to develop ML models for facilitating our daily life would go in vain if the models fail to achieve our trust; thus numerous explanatory ML models have been presented to gain human trust.
What do we mean by — Explainability?
Simply, knowing why the models act the way they do. In other words, an explainable machine learning algorithm should be able to answer questions such as, “Why was your loan/credit card application denied?” Which symptoms and indicators point to a certain diagnosis?
