Model Decision

Model decision research focuses on understanding and improving how models arrive at their conclusions, particularly in complex domains like natural language processing and healthcare. Current research emphasizes evaluating model robustness across diverse datasets and exploring the impact of design choices (e.g., model architecture, training data, evaluation metrics) on final outcomes, often using techniques like multiverse analysis to assess fairness and mitigate bias. This work is crucial for building trustworthy and reliable AI systems, improving their explainability, and ensuring responsible deployment in high-stakes applications where model decisions have significant consequences.