One fact about machine learning and data algorithms that may surprise business users is that there aren’t that many of them.

Algorithms are proprietary though, and monopolistic within their context (a customer can’t select the algorithm they want to use to assess their credit, for instance).

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.

They’re shown examples of things and given labels to associate with what they’re shown. Show a computer (or a child) a picture of a cat, say that’s what a cat looks like, and the algorithm ...

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 ...

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 algorithm will expand to all languages in which Google offers Search, but there is no set timeline, yet, said Google’s Danny Sullivan. A BERT model is also being used to improve featured ...

The first case might occur, for example, if a deep-learning algorithm is fed more photos of light-skinned faces than dark-skinned faces.

In his new book, AI expert Hatim Raham explains how algorithms have created a new labor market paradigm for workers.