550 citations · 909 across the 14 of their papers we have counts for
16 papers
Reconciling the accuracy-diversity trade-off in recommendations
Kenny Peng, Manish Raghavan, Emma Pierson +2
In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items repres…
Choosing the Right Weights: Balancing Value, Strategy, and Noise in Recommender Systems
Smitha Milli, Emma Pierson, Nikhil Garg
Many recommender systems optimize a linear weighting of different user behaviors, such as clicks, likes, and shares. We analyze the optimal choice of weights from the perspectives…
How do Authors' Perceptions of their Papers Compare with Co-authors' Perceptions and Peer-review Decisions?
Charvi Rastogi, Ivan Stelmakh, Alina Beygelzimer +7
How do author perceptions match up to the outcomes of the peer-review process and perceptions of others? In a top-tier computer science conference (NeurIPS 2021) with more than 23,…
Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysis
J. D. Zamfirescu-Pereira, Jerry Chen, Emily Wen +3
Algorithms provide powerful tools for detecting and dissecting human bias and error. Here, we develop machine learning methods to to analyze how humans err in a particular high-sta…
Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting
Divya Shanmugam, Kaihua Hou, Emma Pierson
Estimating the prevalence of a medical condition, or the proportion of the population in which it occurs, is a fundamental problem in healthcare and public health. Accurate estimat…
Assessing racial inequality in COVID-19 testing with Bayesian threshold tests
Emma Pierson
There are racial disparities in the COVID-19 test positivity rate, suggesting that minorities may be under-tested. Here, drawing on the literature on statistically assessing racial…
Ethical Machine Learning in Health Care
Irene Y. Chen, Emma Pierson, Sherri Rose +3
The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical consideratio…
Concept Bottleneck Models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang +4
We seek to learn models that we can interact with using high-level concepts: if the model did not think there was a bone spur in the x-ray, would it still predict severe arthritis?…
Predicting pregnancy using large-scale data from a women's health tracking mobile application
Bo Liu, Shuyang Shi, Yongshang Wu +4
Predicting pregnancy has been a fundamental problem in women's health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the r…
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Emma Pierson, Pang Wei Koh, Tatsunori Hashimoto +4
Modeling how individuals evolve over time is a fundamental problem in the natural and social sciences. However, existing datasets are often cross-sectional with each individual obs…
Demographics and discussion influence views on algorithmic fairness
Emma Pierson
The field of algorithmic fairness has highlighted ethical questions which may not have purely technical answers. For example, different algorithmic fairness constraints are often i…
Modeling Individual Cyclic Variation in Human Behavior
Emma Pierson, Tim Althoff, Jure Leskovec
Cycles are fundamental to human health and behavior. However, modeling cycles in time series data is challenging because in most cases the cycles are not labeled or directly observ…
A large-scale analysis of racial disparities in police stops across the United States
Emma Pierson, Camelia Simoiu, Jan Overgoor +4
To assess racial disparities in police interactions with the public, we compiled and analyzed a dataset detailing over 60 million state patrol stops conducted in 20 U.S. states bet…
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning
Bo Wang, Daniele Ramazzotti, Luca De Sano +3
We here present SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), an open-source tool that implements a novel framework to learn a sample-to-sample similarity measure f…
Fast Threshold Tests for Detecting Discrimination
Emma Pierson, Sam Corbett-Davies, Sharad Goel
Threshold tests have recently been proposed as a useful method for detecting bias in lending, hiring, and policing decisions. For example, in the case of credit extensions, these t…
Algorithmic decision making and the cost of fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller +2
Algorithms are now regularly used to decide whether defendants awaiting trial are too dangerous to be released back into the community. In some cases, black defendants are substant…