Search Results for author: Kristian Lum

Found 6 papers, 2 papers with code

Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems

no code implementations12 Sep 2022 Amanda Bower, Kristian Lum, Tomo Lazovich, Kyra Yee, Luca Belli

Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user.

Fairness Recommendation Systems

Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina

no code implementations30 May 2022 Mohsen Abbasi, Suresh Venkatasubramanian, Sorelle A. Friedler, Kristian Lum, Calvin Barrett

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.

De-biasing "bias" measurement

1 code implementation11 May 2022 Kristian Lum, Yunfeng Zhang, Amanda Bower

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."

Decision Making Fairness

Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics

no code implementations3 Feb 2022 Tomo Lazovich, Luca Belli, Aaron Gonzales, Amanda Bower, Uthaipon Tantipongpipat, Kristian Lum, Ferenc Huszar, Rumman Chowdhury

We show that we can use these metrics to identify content suggestion algorithms that contribute more strongly to skewed outcomes between users.

Estimating the number of SARS-CoV-2 infections and the impact of social distancing in the United States

1 code implementation6 Apr 2020 James Johndrow, Kristian Lum, Maria Gargiulo, Patrick Ball

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

A statistical framework for fair predictive algorithms

no code implementations25 Oct 2016 Kristian Lum, James Johndrow

We propose a method to remove bias from predictive models by removing all information regarding protected variables from the permitted training data.

Cannot find the paper you are looking for? You can Submit a new open access paper.