The Nature of Bias in AI Systems

Biases in generative AI systems stem from the data they are trained on, which reflects human prejudices and stereotypes. When users prompt these AI models, such as those for image generation, the output often perpetuates common biases related to gender, age, and race. For example, a query for images of a computer programmer typically yields mostly male representations, while prompts for childcare workers predominantly result in female images. This highlights the need for automated methods to identify and correct these biases, as relying solely on human observation is insufficient given the rapid and extensive use of these technologies.