Users can set goals for the number and diversity of articles to read each week.
DeBias automatically tracks the user's news sources, displaying viewing history in an intuitive interface that alllows users to quickly recognize their own biases.
Users can share their viewing history with friends and compete to be the most well-read, or to have the most diversity in their news sources.
Using objective, analytical classifers, DeBias automatically categorizes news sources by their inherent biases.
Users can win badges for accomplishing extraordinary tasks, motivating them to go above-and-beyond to change their news consumption habits.
By motivating users to have more variety in their news sources, DeBias is helping to free society from the intense polarization of the past decade.
To create DeBias, we followed the steps taught in CS 147. These first four steps in the process required nominal use of a computer or digital tools, reflecting the importance of old-fashioned, pen-and-paper design, interviewing, and concept visualization techniques.
The next four steps built on the previous ones, allowing us to create a high fidelity prototype for iOS as the final step in the process.
