In our increasingly automated world, complex algorithms are making critical decisions that profoundly impact human lives. They are used to decide who gets a loan, who gets a job interview, what news we see, and even how long a prison sentence should be. These systems are often presented as being objective and impartial, a way to eliminate human error and bias from decision-making. However, a growing body of evidence reveals a critical flaw in this assumption: algorithms can be just as biased as the humans who create them, and often in ways that are far more opaque and difficult to challenge. This phenomenon, known as algorithmic bias, has become one of the most pressing legal and ethical issues of our time.

Algorithmic bias is not a malicious act of a sentient machine. It is a reflection of the data and the design choices of its human creators. There are two primary ways that bias seeps into these systems.

The first, and most common, is through biased training data. Machine learning algorithms learn by analyzing vast datasets of past examples. If this historical data reflects existing societal biases, the algorithm will not only learn those biases but will often amplify them. For example, if an algorithm is trained on historical hiring data from a company that predominantly hired men for engineering roles, the algorithm will learn to associate male candidates with success in that role. It will then begin to systematically penalize female candidates, even if they are equally or more qualified, creating a discriminatory feedback loop. The algorithm is not “sexist”; it is simply a very efficient pattern-recognition machine that has been trained on a biased pattern.