The Fairness-Accuracy Tradeoff

The fairness-accuracy tradeoff refers to the challenge in designing algorithms that make predictions or decisions. Improving fairness—ensuring outcomes don’t unfairly favor or disadvantage any group—can sometimes reduce overall accuracy, which is how correct the predictions are. Conversely, optimizing for maximum accuracy might lead to biased results, unintentionally harming certain groups. Balancing these goals involves choosing how much to prioritize fairness versus accuracy, recognizing that making a model fairer might come at the expense of some precision, and vice versa. The goal is to find a suitable compromise that aligns with ethical and practical priorities.