Model Explainability - Learn Data Science with Travis

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…

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…

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…

Are All Explainable Models Trustworthy?

Towards Data Science

Explainable AI or Explainable Data Science is one of the top buzzwords of Data Science at the moment. Models that are explainable are seen as the answer to many of recently recognised problems with…

Model Explainability, Revisited: SHAP and Beyond

Towards Data Science

The rapid rise of large language models has dominated much of the conversation around AI in recent months—which is understandable, given LLMs’ novelty and the speed of their integration into the daily...

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...

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 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...

Why Model Explainability is The Next Data Science Superpower

Towards Data Science

I’ve interviewed many data scientists in the last 10 years, and model explainability techniques are my favorite topic to distinguish the very best data scientists from the average. Some people think…

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…

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…

The How of Explainable AI: Post-modelling Explainability

Towards Data Science

In the first two parts of our overview of the How of XAI, we looked into pre-modelling explainability and explainable modelling methodologies, which focus on explainability at the dataset stage and…