Algorithms are a staple of modern life. People rely on algorithmic recommendations to wade through deep catalogs and find the best movies, routes, information, products, people and investments.

In recent years, employers have tried a variety of technological fixes to combat algorithm bias — the tendency of hiring and recruiting algorithms to screen out job applicants by race or gender. They ...

Overview Machine learning offers efficiency at scale, but trust depends on understanding how decisions are madeAs machine ...

After months of delays, New York City today began enforcing a law that requires employers using algorithms to recruit, hire or promote employees to submit those algorithms for an independent audit — ...

New research by Questrom’s Carey Morewedge shows that people recognize more of their biases in algorithms’ decisions than they do in their own—even when those decisions are the same Algorithms were ...

When scientists test algorithms that sort or classify data, they often turn to a trusted tool called Normalized Mutual Information (or NMI) to measure how well an algorithm's output matches reality.

AI is increasingly finding its way into healthcare decisions, from diagnostics to treatment decisions to robotic surgery. As I’ve written about in this newsletter many times, AI is sweeping the ...

Algorithms were supposed to make our lives easier and fairer: help us find the best job applicants, help judges impartially assess the risks of bail and bond decisions, and ensure that health care is ...