4 papers
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…
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…
Causal Discovery for Explainable AI: A Dual-Encoding Approach
Henry Salgado, Meagan R. Kendall, Martine Ceberio
Understanding causal relationships among features is fundamental for explaining machine learning model decisions. However, traditional causal discovery methods face challenges with…
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…