11 citations · 22 across the 6 of their papers we have counts for
6 papers
Who Leads in the Shadows? ERGM and Centrality Analysis of Congressional Democrats on Bluesky
Gordon Hew, Ian McCulloh
Following the 2024 U.S. presidential election, Democratic lawmakers and their supporters increasingly migrated from mainstream social media plat-forms like X (formerly Twitter) to…
Recommending Training Set Sizes for Classification
Phillip Koshute, Jared Zook, Ian McCulloh
Based on a comprehensive study of 20 established data sets, we recommend training set sizes for any classification data set. We obtain our recommendations by systematically withhol…
Security and Privacy Considerations for Machine Learning Models Deployed in the Government and Public Sector (white paper)
Nader Sehatbakhsh, Ellie Daw, Onur Savas +2
As machine learning becomes a more mainstream technology, the objective for governments and public sectors is to harness the power of machine learning to advance their mission by r…
Improving Community Resiliency and Emergency Response With Artificial Intelligence
Ben Ortiz, Laura Kahn, Marc Bosch +4
New crisis response and management approaches that incorporate the latest information technologies are essential in all phases of emergency preparedness and response, including the…
AAAI FSS-19: Human-Centered AI: Trustworthiness of AI Models and Data Proceedings
Florian Buettner, John Piorkowski, Ian McCulloh +1
To facilitate the widespread acceptance of AI systems guiding decision-making in real-world applications, it is key that solutions comprise trustworthy, integrated human-AI systems…
Assessing Data Quality of Annotations with Krippendorff Alpha For Applications in Computer Vision
Joseph Nassar, Viveca Pavon-Harr, Marc Bosch +1
Current supervised deep learning frameworks rely on annotated data for modeling the underlying data distribution of a given task. In particular for computer vision algorithms power…