Model Explainability

Model Explainability and JRT AI

Towards Data Science

Model explainability is getting more and more common and mainstream into AI models and usage in today’s scenario. Expectation is to understand what is happening in detail and the “how” part of any…

Picking an explainability technique

Towards Data Science

ML Model Explainability (sometimes referred to as Model Interpretability or ML Model Transparency) is a fundamental pillar of AI Quality. It is impossible to trust a machine learning model without…

If Your Model Isn’t Explainable, Is It Really Your Model?

Towards Data Science

Explainability in machine learning and AI systems is no longer a nice-to-have feature, but an essential component for any product that users and policymakers can consider safe, reliable, and fair…

Machine Learning Models Explainability — definitions, importance, techniques, and tools

Towards AI

Machine Learning Models Explainability — Definitions, Importance, Techniques, And Tools Techniques (LIME, SHAP, PDP, ICE, DeepLIFT, others), libraries, and other details of Model Explainability Photo...

Explainability: The Last Mile

Towards Data Science

For your user to understand your model it’s not enough for it to be ‘explainable’ — you need to provide the ultimate explanation Interpretable or explainable models have gone from being almost a…

Mixing Art into the Science of Model Explainability

Towards Data Science

Overview on Explainable Boosting Machine and an approach for converting ML explanation to more human-friendly explanation. Fig.1 — A lego figure on my desk, Image by the author. 1\. Science of ML exp...

TE2Rules: Explaining “Why did my model say that?”

Towards Data Science

Taking model explainability beyond images and text In the rapidly evolving landscape of artificial intelligence, recent advancements have propelled the field to astonishing heights, enabling models t...

The Meaning of Explainability for AI

Towards Data Science

Do we still care about how our machine learning does what it does? Today I want to get a bit philosophical and talk about how explainability and risk intersect in machine learning. Photo by Kenny Eli...

An overview of model explainability in modern machine learning

Towards Data Science

How we can understand black box machine learning models, and why it matters

The How of Explainable AI: Explainable Modelling

Towards Data Science

In the first part of our overview of the How of Explainable AI, we looked a pre-modelling explainability. However, the true scope of explainability is much broader. Explainability can be considered…

The How of Explainable AI: Pre-modelling Explainability

Towards Data Science

AI explainability is a broad and multi-disciplinary domain, being studied in several fields including machine learning, knowledge representation and reasoning, human-computer interaction, and the…

A Simple Model-Independent Score Explanation Method

Towards Data Science

The exponential growth of Machine Learning (ML) applications and the embedding of models in many production applications drive the need to explain these models' explainability and transparency. In…