Examples abound of AI systems behaving badly. Last year, Amazon was forced to ditch a hiring algorithm that was found to be gender biased; Google was left red-faced after the autocomplete ...

All around us, algorithms are invisibly at work. They’re recommending music and surfacing news, finding cancerous tumors, and making self-driving cars a reality. But do people trust them? Not ...

Examples of algorithm performance with respect to different manatee densities in the scene. The first row shows original images with increasing manatee density from left to right. The second and ...

Last month, Twitter users uncovered a disturbing example of bias on the platform: An image-detection algorithm designed to optimize photo previews was cropping out Black faces in favor of white ones.

A study published Thursday in Science has found that a health care risk-prediction algorithm, a major example of tools used on more than 200 million people in the U.S., demonstrated racial bias ...

The first case might occur, for example, if a deep-learning algorithm is fed more photos of light-skinned faces than dark-skinned faces. The resulting face recognition system would inevitably be ...

Such algorithms are also useful in helping leaders make decisions involving trade-offs. They can present non-obvious choices and opportunities. Other machine learning algorithms examples would ...

I love both of these examples, because I love the idea that we can take our own democratic action to make the world a bit less complicated. Alas, it is not that simple.

A health care algorithm makes black patients substantially less likely than their white counterparts to receive important medical treatment. The major flaw affects millions of patients, and was ...

For example, algorithms used in facial recognition technology have in the past shown higher identification rates for men than for women, and for individuals of non-white origin than for whites.