New research 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 supposed to make our lives ...

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.

Understanding bias in hiring algorithms and ways to mitigate it requires us to explore how predictive technologies work at each step of the hiring process. Though they commonly share a backbone of ...

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.

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 ...

Inequality in the Digital Age: Harvard University Professor Mahzarin Banaji, an experimental psychologist who coined the term “implicit bias” nearly 40 years ago, has developed cutting-edge methods to ...

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 ...