All mainstream credit card numbers obey a mathematical trick designed to catch the most common typos. It’s called the Luhn ...

An enormously high number of algorithms are in use today in various electronic systems. Integrating and evaluating a DSP algorithm with the system is tricky enough to bring programmers to their knees.

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.

Because people train algorithms on their decisions – for example, algorithms that make recommendations on e-commerce and social media sites – algorithms learn and codify human biases.

A sample algorithm available in Algorithmia’s library. The demo code shown here to invoke the API is JavaScript, but Java and Python code can be generated as well.

Some of the algorithms that attract the least attention are capable of inflicting the most harm—for example, algorithms that are woven into the fabric of government services and dictate whether ...

In response, AI researchers have sought to produce algorithms that avoid, or at least minimise, unfairness, for example, by equalising false positive rates across racial groups.

Algorithm-based stock trading is shrouded in mystery at financial firms. A new startup, Quantopian, aims to make these algorithms available to a much larger audience.

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).

Two sample algorithms reached the same conclusion naturally: They should collude.