Search Results for author:
Found 7 papers, 2 papers with code
A statistical framework for fair predictive algorithms
We propose a method to remove bias from predictive models by removing all information regarding protected variables from the permitted training data.
Estimating the number of SARS-CoV-2 infections and the impact of social distancing in the United States
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
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.
De-biasing "bias" measurement
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."
Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina
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.
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.
Bias in Language Models: Beyond Trick Tests and Toward RUTEd Evaluation
In this work, we study the correspondence between such decontextualized "trick tests" and evaluations that are more grounded in Realistic Use and Tangible {Effects (i. e. RUTEd evaluations).
