PA · 2026
When AI models look at a political campaign photo, do they make assumptions about the candidate's ideology based on race or gender — even when the ad itself is completely neutral? …
Working Paper · 2026
Introduces Gaussian process regression (GPR) as a complement to standard linear models in political science, combining theoretical structure with flexibility about functional …
Working Paper · 2026
Proposes GD-GPIRT, a generalized dynamic Gaussian process item response theory model for longitudinal and ordinal observations. The model combines Bayesian nonparametric IRT — …
PSRM · 2025
Proposes a framework for evaluating the validity and trustworthiness of measures created using supervised machine learning. The paper argues that standard validation practices from …
FAccT · 2024
Psychologists have long known that people tend to perceive minority and marginalized groups as more uniform and interchangeable than dominant groups — a phenomenon called …
NeurIPS · 2024
Personality psychology has long been caught between two competing visions: models that describe everyone the same way, and models tailored uniquely to each individual. This paper …
AIStats · 2023
Introduces a Gaussian process framework for estimating treatment effects that vary over time in panel data. The multi-task approach borrows strength across units and time periods, …
PA · 2022
Develops a dynamic Bayesian model for forecasting US Senate elections that integrates polling data, contextual factors, and temporal dynamics. The approach outperforms simpler …
UAI · 2020
Proposes GPIRT, a Gaussian process extension of item response theory that relaxes parametric assumptions about how respondents relate to latent traits. The model provides more …
SAGE Handbook of Research Methods in Political Science and International Relations · 2020
An invited handbook chapter providing a comprehensive overview of Bayesian approaches to model selection, comparison, and averaging for political scientists. Covers practical …