This article explores the phenomenon of algorithm aversion, analyzing how familiarity impacts our judgment of errors made by ...

New inventions—like the printing press, magnetic compasses, steam engines, calculators and the internet—can create radical ...

For example, when the technology publication Rest of World analyzed popular AI image generators in 2023, it found that these programs tended toward ethnic and national stereotypes.

For example, gradient descent is often used in machine learning in ways that don’t require extreme precision. But a machine learning researcher might want to double the precision of an experiment.

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 ...

Another issue is that most ML and deep learning algorithms (that do not receive explicit instructions regarding the outcome) are often still regarded as a 'black box'. For example, at the start of ...

To make that prediction, the algorithm relies on data about how much it costs a care provider to treat a patient. In theory, this could act as a substitute for how sick a patient is.

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 ...

An algorithm widely used in hospitals to steer care prioritizes patients according to health-care spending, resulting in a bias against black patients, a study found.

In 2019, a bombshell study found that a clinical algorithm many hospitals were using to decide which patients need care was showing racial bias — Black patients had to be deemed much sicker than white ...