58 citations · 211 across the 22 of their papers we have counts for
22 papers
The Intersectionality Problem for Algorithmic Fairness
Johannes Himmelreich, Arbie Hsu, Kristian Lum +1
A yet unmet challenge in algorithmic fairness is the problem of intersectionality, that is, achieving fairness across the intersection of multiple groups -- and verifying that such…
Reducing Population-level Inequality Can Improve Demographic Group Fairness: a Twitter Case Study
Avijit Ghosh, Tomo Lazovich, Kristian Lum +1
Many existing fairness metrics measure group-wise demographic disparities in system behavior or model performance. Calculating these metrics requires access to demographic informat…
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…
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…
De-biasing "bias" measurement
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." While mu…
Measuring and mitigating voting access disparities: a study of race and polling locations in Florida and North Carolina
Mohsen Abbasi, Suresh Venkatasubramanian, Sorelle A. Friedler +2
Voter suppression and associated racial disparities in access to voting are long-standing civil rights concerns in the United States. Barriers to voting have taken many forms over…
Characterizing patterns in police stops by race in Minneapolis from 2016-2021
Tuviere Onookome-Okome, Jonah Gorondensky, Eric Rose +3
The murder of George Floyd centered Minneapolis, Minnesota, in conversations on racial injustice in the US. We leverage open data from the Minneapolis Police Department to analyze…
Flipping the Script on Criminal Justice Risk Assessment: An actuarial model for assessing the risk the federal sentencing system poses to defendants
Mikaela Meyer, Aaron Horowitz, Erica Marshall +1
In the criminal justice system, algorithmic risk assessment instruments are used to predict the risk a defendant poses to society; examples include the risk of recidivating or the…
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…
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…
It's COMPASlicated: The Messy Relationship between RAI Datasets and Algorithmic Fairness Benchmarks
Michelle Bao, Angela Zhou, Samantha Zottola +5
Risk assessment instrument (RAI) datasets, particularly ProPublica's COMPAS dataset, are commonly used in algorithmic fairness papers due to benchmarking practices of comparing alg…
Closer than they appear: A Bayesian perspective on individual-level heterogeneity in risk assessment
Kristian Lum, David B. Dunson, James Johndrow
Risk assessment instruments are used across the criminal justice system to estimate the probability of some future behavior given covariates. The estimated probabilities are then u…
Estimating the number of SARS-CoV-2 infections and the impact of social distancing in the United States
James Johndrow, Kristian Lum, Maria Gargiulo +1
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 limi…
The impact of overbooking on a pre-trial risk assessment tool
Kristian Lum, Chesa Boudin, Megan Price
Pre-trial risk assessment tools are used to make recommendations to judges about appropriate conditions of pre-trial supervision for people who have been arrested. Increasingly, th…
Removing the influence of a group variable in high-dimensional predictive modelling
Emanuele Aliverti, Kristian Lum, James E. Johndrow +1
In many application areas, predictive models are used to support or make important decisions. There is increasing awareness that these models may contain spurious or otherwise unde…
The causal impact of bail on case outcomes for indigent defendants
Kristian Lum, Mike Baiocchi
We use near-far matching, a technique for estimating causal relationships, to explore whether bail causes a higher likelihood of conviction. We find evidence of a strong causal imp…
An algorithm for removing sensitive information: application to race-independent recidivism prediction
James E. Johndrow, Kristian Lum
Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing or augmenting human judgment with computer models in high s…
Theoretical Limits of Record Linkage and Microclustering
James E. Johndrow, Kristian Lum, David B. Dunson
There has been substantial recent interest in record linkage, attempting to group the records pertaining to the same entities from a large database lacking unique identifiers. This…
A statistical framework for fair predictive algorithms
Kristian Lum, James Johndrow
Predictive modeling is increasingly being employed to assist human decision-makers. One purported advantage of replacing human judgment with computer models in high stakes settings…
Estimating the observable population size from biased samples: a new approach to population estimation with capture heterogeneity
James E. Johndrow, Kristian Lum, Daniel Manrique-Vallier
Capture-recapture methods aim to estimate the size of a closed population on the basis of multiple incomplete enumerations of individuals. In many applications, the individual prob…
An agent-based epidemiological model of incarceration
Kristian Lum, Samarth Swarup, Stephen Eubank +1
We build an agent-based model of incarceration based on the SIS model of infectious disease propagation. Our central hypothesis is that the observed racial disparities in incarcera…
Bayesian variable selection for spatially dependent generalized linear models
Kristian Lum
Despite the abundance of methods for variable selection and accommodating spatial structure in regression models, there is little precedent for incorporating spatial dependence in…