Author: Paul Spiegelhalter

As machine learning becomes more widely used for automated decision-making, we must identify issues of fairness in ML outcomes. Ensuring fairness in ML is important for several reasons; lack of fairness in machine learning can perpetuate or amplify societal biases…

My previous blog post considered what we can learn about learning by watching computers master video games. What’s interesting is that the computer in that case wasn’t an “expert” at video games. It hadn’t made a comprehensive study of all…

In our first blog post in this series we looked at the difference between machine memorization and machine learning. So what does that learning look like in action, and what are the implications for data science? There was a great…