Despite the meteoric rise of ML, most commercially available datasets only represent a small fraction of humanity, with a skew towards high-income populations. To combat algorithmic bias, businesses need to train their ML algorithms on datasets that are representative of all the populations that will be affected by AI deployment.

This presentation will discuss how socioeconomically diverse datasets can help address algorithmic bias, drawing from Cody’s experience co-creating the open-access Dollar Street Dataset (alongside Gapminder, Harvard University, and MLCommons) and his deep academic knowledge in the space.