HSOC-267A: Truth, Lies & Data: Age of AI

Data is all around us. Data on climate change, economics, mental health, gaming, trends in fashion, music, and art. Some of it is accurate; some of it isn't. Influencers exaggerate data claims. Articles and podcasts cite data points out of context. And AI, at least sometimes, outright makes up data. Perhaps more insidious, research studies themselves are sometimes biased: Non-representative samples, skewed questions, and misleading analysis. And more insidious still, our own psychology-and innate cognitive biases-can lead us astray when it comes to interpreting data accurately. In this course, we will learn how to evaluate data with a critical eye. How to determine when it's lying, when it's true, and when it's half-true. And we will learn how to gather, organize, and interpret our own quantitative data. At the same time, we will explore both current and emerging topics related to truth in data, such as data ethics, cross-cultural considerations, misinformation, algorithmic bias, and synthetic respondent data. A large portion of the class will be dedicated to conducting a custom research project.