black box Archives

black box

White and Black Box Testing Tips for 2022

Modeling black box White boxposted by ODSC Community February 14, 2022

White box testing, also called transparent testing because the tester is aware of the internal structure of the system or application, is used to remedy any defects that are discovered. It is essential because it resolves gaps proactively by implementing a forward-thinking methodology. The purpose of... Read more

Making Explainability Work in Practice

Blogs from ODSC Speakers Conferences Modeling black box East 2021 Explainability explainable AIposted by ODSC Community March 17, 2021

Complex ‘black box’ models are becoming more and more prevalent in industries involving high-stakes decisions (such as finance, healthcare, insurance). As machine learning algorithms take a prominent role in our daily lives, explaining their decision will only grow in importance via explainability. By now there is... Read more

Black Box Optimization Using Latent Action Monte Carlo Tree Search (LaMCTS)

Blogs from ODSC Speakers Conferences Modeling black box East 2021posted by ODSC Community March 9, 2021

Black box optimization has numerous applications in industries. From a/b testing to experimental designs of new ads or UI, hyper-parameter tuning in the machine learning models, or to find the optimal configuration of a system, black-box optimization tries to optimize your decision solely by exploring the... Read more

AI Black Box Horror Stories – When Transparency was Needed

Machine Learning Modeling black boxposted by Daniel Gutierrez, ODSC October 2, 2019

Arguably, one of the biggest debates happening in data science in 2019 is the need for AI explainability. The ability to interpret machine learning models is turning out to be a defining factor for the acceptance of statistical models for driving business decisions. Enterprise stakeholders are... Read more

Interpretability and the Rise of Shapley Values

Blogs from ODSC Speakers Conferences Featured Post black box West 2019posted by Rajiv Shah August 30, 2019

Interpretability is a hot topic in data science this year. Earlier this spring, I presented at ODSC East on the need for data scientists to use best practices like permutation-based importance, partial dependence, and explanations. When I first put together this talk, a lot of it... Read more

Cracking the Box: Interpreting Black Box Machine Learning Models

Machine Learning Modeling black box black box machine learning Machine Learningposted by Yuriy Gavrilin August 7, 2019

Intro To kick off this article, I’d like to explain the interpretability of a machine learning (ML) model. According to Merriam-Webster, interpretability describes the process of making something plain or understandable. In the context of ML, interpretability provides us with an understandable explanation of how a... Read more

Not Always a Black Box: Machine Learning Approaches For Model Explainability

Blogs from ODSC Speakers Conferences Guest contributor Modeling black box Interpretabilityposted by Violeta Misheva August 5, 2019

Editor’s Note: Violeta is speaking at ODSC Europe 2019, see her talk “Not Always a Black Box: Explainability Applications for a Real Estate Problem“ What is model explainability? Imagine that you have built a very precise machine learning model by using clever tricks and non-standard features.... Read more

Innovators and Regulators Collaborate on Book Tackling AI’s Black Box Problem

Tech Updates AI Artificial Intelligence black boxposted by Alex Detmering October 9, 2018

AI’s Biggest Compliance Hurdle If you’re in data science, machine learning, or AI, you’ve probably heard of the “black box” problem. In short, it is the regulatory and implementation barriers caused by the un-explainability of sophisticated AI. Why are sophisticated AI systems so hard to explain?... Read more