AI In Society
AI systems are shaping our world, but who builds them, and who do they serve? These lessons allow students to explore the connections between machine learning, community, and society. Students critically examine how datasets used to train AI systems can influence who is seen and and who is represented.
Art Selfies & Bias
This activity helps students see first-hand how bias can be baked into ML applications. By engaging with an art selfie app and exploring its dataset, students recognize how underrepresentation in global art collections affects who is seen and recognized. The lesson encourages critical thinking about data sources, digital inclusion, and the importance of building more balanced, representative datasets in ML development.
Unanticipated Consequences
Machine learning systems can have unintended consequences when they are built using limited or biased data. This activity helps students think critically about how the choices developers make can shape how AI systems behave and who they work well for.