3 papers
cs.AI2026
A Causal Argumentation Method for Explainability of Machine Learning Models
Henry Salgado, Meagan R. Kendall, Martine Ceberio
Explainable AI (XAI) methods identify which features are relevant to a model's predictions but often fail to clarify why certain decisions are made. In this work, we present a nove…
cs.HC2026
LLMs in Qualitative Research: Opportunities, Limitations, and Practical Considerations
Henry Salgado, Meagan R. Kendall, Martine Ceberio +1
This paper examines the opportunities, limitations, and practical considerations associated with the use of large language models (LLMs) in qualitative research. Drawing on a multi…
cs.LG2025
Does the Model Say What the Data Says? A Simple Heuristic for Model Data Alignment
Henry Salgado, Meagan R. Kendall, Martine Ceberio
In this work, we propose a simple and computationally efficient framework for evaluating whether machine learning models align with the structure of the data they learn from; that…