Ryan Steed

Research

My research interests span algorithmic bias, private & fair machine learning, and public policy.

I am co-advised by Alessandro Acquisti and Alexandra Chouldechova. My undergraduate work was advised by Aylin Caliskan, Rahul Simha, and Ben Williams.

Image Representations Learned With Unsupervised Pre-Training Contain Human-like Biases ⋅ 2021
Proceedings of the ACM Conference on Fairness, Accountability, and Transparency (FAccT 2021).
With Aylin Caliskan.
Vice , MIT Technology Review & OneZero.
A Set of Distinct Facial Traits Learned by Machines Is Not Predictive of Appearance Bias in the Wild ⋅ 2021
AI Ethics.
With Aylin Caliskan.
Heuristic-Based Weak Learning for Automated Decision-Making ⋅ 2020
Participatory Approaches to Machine Learning @ ICML 2020.
With Ben Williams.
A Deep Learning Approach for CAMEO Coding ⋅ 2017
International Studies Association South Annual Conference, Orlando.
With Max Erdemandi, Rachel Augustine & Lin Zhao.

Heuristic-Based Weak Learning for Moral Decision-Making ⋅ 2020
Poster presentation at Participatory Approaches to Machine Learning @ ICML 2020.
With Ben Williams.
The Grand Narrative: A History of Economics Through Data ⋅ 2020
Curriculum.
With Rahul Simha, Ruoyu Chen, Kristin Carline & Bill Edison.
Evolution of Innovation in Patent Citation Networks ⋅ 2019
2^nd Prize, GW Research Showcase. Top 5% of submissions.
ML Document Classification ⋅ 2018
Tech talk presented at GWU ACM.