We carry around different cognitive biases and assumptions about the world around us, but how do these biases affect us. In this session, we will explore a machine-learning tool that can label and classify visual data. By thinking how bias can affect machine-learning tools, we can in turn think about how our own biases impact our own thoughts.

Additionally, AI algorithms have been shown to possess cultural and racial bias because of the way they are generated and trained. In this session we will identify these biases and discuss ways to make AI research and education readily available for a diverse and inclusive audience.