38 citations · 107 across the 19 of their papers we have counts for
3 papers · 1 filter
Measure Utility, Gain Trust: Practical Advice for XAI Researcher
Brittany Davis, Maria Glenski, William Sealy +1
Research into the explanation of machine learning models, i.e., explainable AI (XAI), has seen a commensurate exponential growth alongside deep artificial neural networks throughou…
Adjusting for Confounders with Text: Challenges and an Empirical Evaluation Framework for Causal Inference
Galen Weld, Peter West, Maria Glenski +3
Causal inference studies using textual social media data can provide actionable insights on human behavior. Making accurate causal inferences with text requires controlling for con…
CrossCheck: Rapid, Reproducible, and Interpretable Model Evaluation
Dustin Arendt, Zhuanyi Huang, Prasha Shrestha +3
Evaluation beyond aggregate performance metrics, e.g. F1-score, is crucial to both establish an appropriate level of trust in machine learning models and identify future model impr…