Algorithmic Influence on (Re)Search

Simulation: Snapshots from the Facebook feeds of Citizen Browser panelists illuminate how Facebook’s recommendation algorithm siloes information on the platform

Article: One Year After the Capitol Riot, Americans Still See Two Very Different Facebooks. Data from The Markup’s Citizen Browser shows the platform’s partisan divide continues

Simulation: Theirtube is a Youtube filter bubble simulator that provides a look into how videos are recommended on other people's YouTube. Users can experience how the YouTube home page would look for six different personas. Each persona simulates the viewing environment of real Youtube users who experienced being inside a recommendation bubble through recreating a Youtube account with a similar viewing history.

Article: We’re not going to get rid of machine learning; we just have to learn how to make it safer, more tightly inspected, and lower the externality costs.

Instructions: Select one phrase to search for in Google images. The phrases are: God, working mom, Mexican, lead singer, doctor, handsome, professor, engineer, cute children, happy family, or expert. Scan the results and reflect on what perspective is represented by these searches? What narrative is being told in the results?

Article: We use Google’s image search to help us understand the world around us. For example, a search about a certain profession, “truck driver” for instance, should yield images that show us a representative smattering of people who drive trucks for a living. How well does it do so?