Algorithmic predictions are ubiquitous these days—think of Amazon recommending a book based on past purchases. More controversial use arises when algorithms incorporate not just personal history, but information about people generally, blurring the lines of personal causation and broad, population-level trends.
More and more decisions are made using machine learning algorithms, which, in theory, can be useful and objective. In reality, says Kay Mathiesen, associate professor of philosophy and religion at Northeastern, “data is biased—because it’s data coming from human beings.”
