Making Explainability Work in Practice
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
Exploring the Moral and Ethical Perspective of a Dataset while Building an Explainable AI Solution
Developing AI code in the 2010s relied on knowledge and talent. Developing AI code in the 2020s implies the accountability of XAI for every aspect of an AI project. It includes moral, ethical, legal, and technical perspectives, all for building an explainable AI solution. [Related article:... Read more
How to Explain Your ML Models?
Explainability in machine learning (ML) and artificial intelligence (AI) is becoming increasingly important. With the increased demand for explanations and the number of new approaches out there, it could be difficult to know where to start. In this post, we will get hands-on experience in explaining... Read more
Are All Explainable Models Trustworthy?
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 recognized problems with machine learning, such as bias or data leaks. [Related Article: The Importance... Read more
7 Top Data Science Trends in 2020 to Be Excited About
As a practicing data scientist, educator, and tech journalist, I automatically have three big motivations for keeping a constant pulse of the industry. Staying in tune with what’s happening, consuming all the news from the movers and shakers, and evaluating new and updated tools as they... Read more
IBM Research Launches Explainable AI Toolkit
Explainability or interpretability of AI is a huge deal these days, especially due to the rise in the number of enterprises depending on the decisions made by machine learning and deep learning. Naturally, stakeholders want a level of transparency for how the algorithms came up with... Read more
Learn Interpretability for Data Science
Editor’s note: Rajiv Shaw will be a speaker at ODSC East 2019 this May! Be sure to check out his talk, “Deciphering the Black Box: Latest Tools and Techniques for Interpretability” there. The impact of machine learning has been tremendous, whether it’s measured in dollars (trillions)... Read more
Explainable AI: From Prediction To Understanding
It’s not enough to make predictions. Sometimes, you need to generate a deep understanding. Just because you model something doesn’t mean you really know how it works. In classical machine learning, the algorithm spits out predictions, but in some cases, this isn’t good enough. Dr. George... Read more
The Importance of Explainable AI
AI algorithms can be trained to perform many disparate tasks, but these systems often are opaque and operate in a black box, meaning users don’t always know how decisions are being made. AI-powered systems, frequently using deep learning methods, can be given extraordinarily complex tasks and... Read more