5 citations · 6 across the 4 of their papers we have counts for
4 papers
Navigating Global AI Regulation: A Multi-Jurisdictional Retrieval-Augmented Generation System
Courtney Ford, Ojas Rane, Susan Leavy
Navigating AI regulation across jurisdictions is increasingly difficult for policymakers, legal professionals, and researchers. To address this, we present a multi-jurisdictional R…
Feature-Guided Neighbor Selection for Non-Expert Evaluation of Model Predictions
Courtney Ford, Mark T. Keane
Explainable AI (XAI) methods often struggle to generate clear, interpretable outputs for users without domain expertise. We introduce Feature-Guided Neighbor Selection (FGNS), a po…
Explaining Classifications to Non Experts: An XAI User Study of Post Hoc Explanations for a Classifier When People Lack Expertise
Courtney Ford, Mark T Keane
Very few eXplainable AI (XAI) studies consider how users understanding of explanations might change depending on whether they know more or less about the to be explained domain (i.…
Play MNIST For Me! User Studies on the Effects of Post-Hoc, Example-Based Explanations & Error Rates on Debugging a Deep Learning, Black-Box Classifier
Courtney Ford, Eoin M. Kenny, Mark T. Keane
This paper reports two experiments (N=349) on the impact of post hoc explanations by example and error rates on peoples perceptions of a black box classifier. Both experiments show…