Researchers at MIT developed a tool that eliminates a source of bias in simulations. Researchers can easily and unknowingly select an algorithm that performs worse on actual data than the simulation predicted that it should. The new…

If a machine-learning model is biased, it may be impossible to remove this bias later. If, for example, an unbalanced dataset focuses on skin color, predictions will be both unfair and unreliable in the real world. MIT researchers have…