But even the smartest algorithms can sometimes behave in unexpected and unwanted ways—for example, picking up gender bias from the text or images they are fed.

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.

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.

One worry, for example, is that advanced natural-language algorithms like GPT-3 could supercharge online disinformation. Another is that large-scale algorithms also contain built-in bias and that it ...