67 citations · 111 across the 8 of their papers we have counts for
8 papers
County-level Algorithmic Audit of Racial Bias in Twitter's Home Timeline
Luca Belli, Kyra Yee, Uthaipon Tantipongpipat +3
We report on the outcome of an audit of Twitter's Home Timeline ranking system. The goal of the audit was to determine if authors from some racial groups experience systematically…
A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter
Kyra Yee, Alice Schoenauer Sebag, Olivia Redfield +3
Harmful content detection models tend to have higher false positive rates for content from marginalized groups. In the context of marginal abuse modeling on Twitter, such dispropor…
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Amanda Bower, Kristian Lum, Tomo Lazovich +2
Traditionally, recommender systems operate by returning a user a set of items, ranked in order of estimated relevance to that user. In recent years, methods relying on stochastic o…
Measuring Disparate Outcomes of Content Recommendation Algorithms with Distributional Inequality Metrics
Tomo Lazovich, Luca Belli, Aaron Gonzales +5
The harmful impacts of algorithmic decision systems have recently come into focus, with many examples of systems such as machine learning (ML) models amplifying existing societal b…
Algorithmic Amplification of Politics on Twitter
Ferenc Huszár, Sofia Ira Ktena, Conor O'Brien +3
Content on Twitter's home timeline is selected and ordered by personalization algorithms. By consistently ranking certain content higher, these algorithms may amplify some messages…
The 2021 RecSys Challenge Dataset: Fairness is not optional
Luca Belli, Alykhan Tejani, Frank Portman +10
After the success the RecSys 2020 Challenge, we are describing a novel and bigger dataset that was released in conjunction with the ACM RecSys Challenge 2021. This year's dataset i…
Causal Inference Struggles with Agency on Online Platforms
Smitha Milli, Luca Belli, Moritz Hardt
Online platforms regularly conduct randomized experiments to understand how changes to the platform causally affect various outcomes of interest. However, experimentation on online…
Fighting Redundancy and Model Decay with Embeddings
Dan Shiebler, Luca Belli, Jay Baxter +2
Every day, hundreds of millions of new Tweets containing over 40 languages of ever-shifting vernacular flow through Twitter. Models that attempt to extract insight from this fireho…