What is meant by algorithm bias?

Algorithm bias refers to unfair outcomes that arise from biased training data. This occurs when the dataset used to train an algorithm contains prejudices or reflects historical inequality, leading the algorithm to make decisions that are systematically unfair to certain groups of individuals. For instance, if an algorithm is trained on data that predominantly features successful individuals from one demographic, it may disadvantage other groups because it lacks representation or context regarding their experiences and needs.

When the training data is skewed, the algorithm learns patterns that can perpetuate existing biases, leading to outcomes that might favor one group over another based on attributes like race, gender, or socioeconomic status. This is especially critical in applications like hiring algorithms or facial recognition systems, where biased training data could result in discrimination or misrepresentation of certain populations. Understanding algorithm bias is essential for developing fair and equitable AI systems, as it underscores the importance of using diverse and representative datasets during the training process.