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.

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 ...

For example, a machine-learning algorithm could be designed to identify patterns in images, and training data could be images of dogs. The resulting machine-learning model would be a dog spotter.

If, for example, you wanted to increase the total number of recommendations a specific piece of content got from the algorithm, all you’d have to do is sign up for X amount of Netflix accounts ...

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 ...

Another example of clustering algorithms in use is recommender systems, which group together users with similar viewing, browsing, or shopping patterns to recommend similar content.

Algorithms may entrench our biases, homogenize and flatten culture, and exploit and suppress the vulnerable and marginalized. But these aren’t completely inscrutable systems or inevitable outcomes.

But Google's DeepMind AI group has now developed a reinforcement learning tool that can develop extremely optimized algorithms without first being trained on human code examples.