From medical diagnoses to credit underwriting, machine learning models are being used to make increasingly important decisions. To trust the systems powered by these models we need to know how they make predictions. This is why the difference between an interpretable and explainable model is important. The way we understand our models and degree to which we can truly understand then depends on whether they are interpretable or explainable. Put briefly, an interpretable model can be understood by a human without any other aids/techniques.

Should we always trust a model that performs well? A model could reject your application for a mortgage or diagnose you with cancer. The consequences of these decisions are serious and, even if they are correct, we would expect an explanation. A human would be able to tell you that your income is too low for a mortgage or that a specific cluster of cells is likely malignant. A model that provided similar explanations would be more useful than one that just provided predictions. By obtaining these explanations, we say we are interpreting a machine learning model.

Should we always trust a model that performs well? A model could reject your application for a mortgage or diagnose you with cancer. The consequences of these decisions are serious and, even if they are correct, we would expect an explanation. A human would be able to tell you that your income is too low for a mortgage or that a specific cluster of cells is likely malignant. A model that provided similar explanations would be more useful than one that just provided predictions.

Explainable machine learning (ML) has been implemented in numerous open source and proprietary software packages and explainable ML is an important aspect of commercial predictive modeling. However, explainable ML can be misused, particularly as a faulty safeguard for harmful black-boxes, e.g. fairwashing, and for other malevolent purposes like model stealing. This text discusses definitions, examples, and guidelines that promote a holistic and human-centered approach to ML which includes interpretable (i.e. white-box ) models and explanatory, debugging, and disparate impact analysis techniques.

Machine Learning models are frequently seen as black boxes that are impossible to decipher. Because the learner is trained to respond to "yes" and "no" type questions without explaining how the answer was obtained. An explanation of how an answer was achieved is critical in many applications for assuring confidence and openness. Explainable AI refers to strategies and procedures in the use of artificial intelligence technology (AI) that allow human specialists to understand the solution's findings. This article will focus on explaining the machine learner using OmniXAI.