475 citations · 1.2k across the 20 of their papers we have counts for
20 papers
Bridging Predictions and Interventions: An Integrated Framework for Automated Decision-Systems
Inioluwa Deborah Raji, Lydia T. Liu, Angela Zhou +27
Automated decision systems (ADS) leverage predictions about individual future outcomes to inform consequential decision-making in organizational settings. Across various settings -…
FINEST: Stabilizing Recommendations by Rank-Preserving Fine-Tuning
Sejoon Oh, Berk Ustun, Julian McAuley +1
Modern recommender systems may output considerably different recommendations due to small perturbations in the training data. Changes in the data from a single user will alter the…
Predictive Churn with the Set of Good Models
Jamelle Watson-Daniels, Flavio du Pin Calmon, Alexander D'Amour +3
Issues can arise when research focused on fairness, transparency, or safety is conducted separately from research driven by practical deployment concerns and vice versa. This separ…
Prediction without Preclusion: Recourse Verification with Reachable Sets
Avni Kothari, Bogdan Kulynych, Tsui-Wei Weng +1
Machine learning models are often used to decide who receives a loan, a job interview, or a public benefit. Models in such settings use features without considering their actionabi…
Algorithmic Censoring in Dynamic Learning Systems
Jennifer Chien, Margaret Roberts, Berk Ustun
Dynamic learning systems subject to selective labeling exhibit censoring, i.e. persistent negative predictions assigned to one or more subgroups of points. In applications like con…
Participatory Personalization in Classification
Hailey Joren, Chirag Nagpal, Katherine Heller +1
Machine learning models are often personalized with information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people but do…
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction
Vinith M. Suriyakumar, Marzyeh Ghassemi, Berk Ustun
Machine learning models are often personalized with categorical attributes that are protected, sensitive, self-reported, or costly to acquire. In this work, we show models that are…
Predictive Multiplicity in Probabilistic Classification
Jamelle Watson-Daniels, David C. Parkes, Berk Ustun
Machine learning models are often used to inform real world risk assessment tasks: predicting consumer default risk, predicting whether a person suffers from a serious illness, or…
Learning Optimal Predictive Checklists
Haoran Zhang, Quaid Morris, Berk Ustun +1
Checklists are simple decision aids that are often used to promote safety and reliability in clinical applications. In this paper, we present a method to learn checklists for clini…
Rank List Sensitivity of Recommender Systems to Interaction Perturbations
Sejoon Oh, Berk Ustun, Julian McAuley +1
Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual dat…
Predictive Multiplicity in Classification
Charles T. Marx, Flavio du Pin Calmon, Berk Ustun
Prediction problems often admit competing models that perform almost equally well. This effect challenges key assumptions in machine learning when competing models assign conflicti…
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions
Hao Wang, Berk Ustun, Flavio P. Calmon
When the performance of a machine learning model varies over groups defined by sensitive attributes (e.g., gender or ethnicity), the performance disparity can be expressed in terms…
Actionable Recourse in Linear Classification
Berk Ustun, Alexander Spangher, Yang Liu
Machine learning models are increasingly used to automate decisions that affect humans - deciding who should receive a loan, a job interview, or a social service. In such applicati…
On the Direction of Discrimination: An Information-Theoretic Analysis of Disparate Impact in Machine Learning
Hao Wang, Berk Ustun, Flavio P. Calmon
In the context of machine learning, disparate impact refers to a form of systematic discrimination whereby the output distribution of a model depends on the value of a sensitive at…
Learning Optimized Risk Scores
Berk Ustun, Cynthia Rudin
Risk scores are simple classification models that let users make quick risk predictions by adding and subtracting a few small numbers. These models are widely used in medicine and…
Interpretable Classification Models for Recidivism Prediction
Jiaming Zeng, Berk Ustun, Cynthia Rudin
We investigate a long-debated question, which is how to create predictive models of recidivism that are sufficiently accurate, transparent, and interpretable to use for decision-ma…
Supersparse Linear Integer Models for Optimized Medical Scoring Systems
Berk Ustun, Cynthia Rudin
Scoring systems are linear classification models that only require users to add, subtract and multiply a few small numbers in order to make a prediction. These models are in widesp…
Methods and Models for Interpretable Linear Classification
Berk Ustun, Cynthia Rudin
We present an integer programming framework to build accurate and interpretable discrete linear classification models. Unlike existing approaches, our framework is designed to prov…
Supersparse Linear Integer Models for Predictive Scoring Systems
Berk Ustun, Stefano Traca, Cynthia Rudin
We introduce Supersparse Linear Integer Models (SLIM) as a tool to create scoring systems for binary classification. We derive theoretical bounds on the true risk of SLIM scoring s…
Supersparse Linear Integer Models for Interpretable Classification
Berk Ustun, Stefano Tracà, Cynthia Rudin
Scoring systems are classification models that only require users to add, subtract and multiply a few meaningful numbers to make a prediction. These models are often used because t…