In 1998, I unintentionally created a racially biased artificial intelligence algorithm. There are lessons in that story that resonate even more strongly today. The systems often fail on women of color ...

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 ...

Human bias, when shaped by values and informed by experience, can be a form of wisdom that protects us from making poor ...

For more than a decade, journalists and researchers have been writing about the dangers of relying on algorithms to make weighty decisions: who gets locked up, who gets a job, who gets a loan — even ...

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.

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 ...

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.

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 ...

(THE CONVERSATION) In 1998, I unintentionally created a racially biased artificial intelligence algorithm. There are lessons in that story that resonate even more strongly today. Facial recognition ...

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 ...