Explainability

Dive into Integrated Gradients method. How the values are calculated? What are the different baselines? — What is Integrated Gradients method? Integrated Gradients ( IG) [1] is a method proposed by Sundararajan et al. that is based on two axioms: Sensitivity and Implementation Invariance. Authors argue that those two axioms should be satisfied by all attribution methods. The definition of those two axioms is as follows:

This is a quick guide to the basics of the data analysis using Pandas and Python as a complete beginner in data science, including code samples and cheatsheets (classification field). — The Pandas library is one of the most popular tool for data scientists to do data manipulation and analysis, and in combination with matplotlib for data visualization it lays the data science foundation. Dataset We will use personal key indicators of heart disease dataset. …

With the rise of digitalization, one of the key challenges of any company is to understand well enough the impact of the algorithms that it uses. Although we hear a lot about highly sophisticated algorithms such as neural networks, often times in big industries algorithms based on simple rules are…

Introduction As part of a graduation project, I had the chance to work with the startup Golem.ai on a rather unusual NLP subject that can be related to explainability. …

Three steps to understand how data and machine learning algorithms can explain when and why an industrial equipment is going to fail. Data Driven Predictive Maintenance: what and why There exist different types of maintenance strategies, such as: Planned Maintenance: allows to plan regular maintenance interventions to check the machines status and to avoid them reaching faulty…

We jointly held an information session with our partner Capital One for current CURP students this past Friday, March 25 via Zoom. For those who may not be familiar with CURP, the CDS Undergraduate Research Program, is a research mentorship program that provides talented students with the opportunity to work…

Machine learning models are usually treated as black boxes, in the sense that they can make good predictions but don’t reason the logic behind it. But making predictions only isn’t enough all the time. Machine learning engineers should also be able to explain why the prediction is either bad or…

Dr Srinivas Padmanabhuni testAIng.com In recent past one of the hot areas in AI has been the domain of Explainable AI also known as XAI. This initiative started by Darpa in US is aimed at making AI and decisions taken by AI systems more acceptable to humans. A key thought…

Explainable Artificial Intelligence (XAI) is an emerging field of artificial intelligence that focuses on the development an AI models that can explain their behavior on some level to human collaborators. …