Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.
In this paper, we quantify access to polling locations, developing a methodology for the calibrated measurement of racial disparities in polling location "load" and distance to polling locations.
When a model's performance differs across socially or culturally relevant groups--like race, gender, or the intersections of many such groups--it is often called "biased."
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.
Understanding the number of individuals who have been infected with the novel coronavirus SARS-CoV-2, and the extent to which social distancing policies have been effective at limiting its spread, are critical for effective policy going forward.
Applications Populations and Evolution