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

Another possibility is to present a number of non-machine-learned algorithm examples and incorporate the people’s responses to those in the survey as well.

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.

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).

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 ...

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 ...