- Explaining the Explainable AI: A 2-Stage Approach - Oct 29, 2020.
Understanding how to build AI models is one thing. Understanding why AI models provide the results they provide is another. Even more so, explaining any type of understanding of AI models to humans is yet another challenging layer that must be addressed if we are to develop a complete approach to Explainable AI.
- Explainability: Cracking open the black box, Part 1 - Dec 4, 2019.
What is Explainability in AI and how can we leverage different techniques to open the black box of AI and peek inside? This practical guide offers a review and critique of the various techniques of interpretability.
- Why the ‘why way’ is the right way to restoring trust in AI - Oct 8, 2019.
As so many more organizations now rely on AI to deliver services and consumer experiences, establishing a public trust in the AI is crucial as these systems begin to make harder decisions that impact customers.
- Opening Black Boxes: How to leverage Explainable Machine Learning - Aug 1, 2019.
A machine learning model that predicts some outcome provides value. One that explains why it made the prediction creates even more value for your stakeholders. Learn how Interpretable and Explainable ML technologies can help while developing your model.
- A Data Science Playbook for explainable ML/xAI - Jul 30, 2019.
This technical webinar on Aug 14 discusses traditional and modern approaches for interpreting black box models. Additionally, we will review cutting edge research coming out of UCSF, CMU, and industry.
- “Please, explain.” Interpretability of machine learning models - May 9, 2019.
Unveiling secrets of black box models is no longer a novelty but a new business requirement and we explain why using several different use cases.
- Machine Learning and Deep Link Graph Analytics: A Powerful Combination - Apr 23, 2019.
We investigate how graphs can help machine learning and how they are related to deep link graph analytics for Big Data.
- An introduction to explainable AI, and why we need it - Apr 15, 2019.
We introduce explainable AI, why it is needed, and present the Reversed Time Attention Model, Local Interpretable Model-Agnostic Explanation and Layer-wise Relevance Propagation.
- XAI – A Data Scientist’s Mouthpiece - Apr 1, 2019.
We outline the usefulness of Explainable AI, which allows you to explain the results of a multidimensional model - including having a multimodal decision boundary - to a business user.
- Explainable AI or Halting Faulty Models ahead of Disaster - Mar 27, 2019.
A brief overview of a new method for explainable AI (XAI), called anchors, introduce its open-source implementation and show how to use it to explain models predicting the survival of Titanic passengers.
- Explainable Artificial Intelligence - Jan 10, 2019.
We outline the necessity of explainable AI, discuss some of the methods in academia, take a look at explainability vs accuracy, investigate use cases, and more.