Behind the Feed Data Challenge

Every day, algorithms decide what shows up in front of us online. The social media posts that appear in your feed, the news stories recommended to you, the videos, music, and products that platforms think you’ll want to see next. 

 

While personalized algorithms can help us find things we care about, it can also shape our view of the world in ways we don't always notice. Some perspectives may get more attention than others, certain communities may be underrepresented, and our feeds can become narrower over time. This could unintentionally reinforce stereotypes, restrict our view of the world or limit our awareness of new ideas. 

 

Your challenge: 

Use data to investigate what information different people and communities get to see, and what they might be missing. 

 

Explore questions such as: 

1) Who gets exposed to what types of information?  

2) Are certain topics, perspectives or sources underrepresented in specific communities?  

3) Does personalization increase relevance at the expense of diversity? 

4) Could recommendation systems unintentionally reinforce stereotypes or limit people's awareness of new ideas and opportunities? 

5) How can we measure whether an information environment is diverse and fair? 

 

Then, use your findings to develop a data-informed solution, tool, framework or strategy that explores how we could make the information people encounter online more transparent, diverse and equitable.