Algorithmic gender discrimination describes how certain AI tools may be harmfully biased against certain groups based on their gender. With or without intent, existing biases can be built into these systems.

Machine learning algorithms process vast quantities of data and rely on accurate information just as human intelligence does. It is humans who generate, collect, and label the data that goes into datasets, and it is humans who determine which datasets, variables, and rules the algorithms learn from to make predictions. Therein lies the problem. According to an article in the Stanford Social Innovation Review, “both of these stages can introduce biases that become embedded in AI systems.”