Model Transparency

Model transparency, aiming to make the decision-making processes of complex machine learning models understandable, is a crucial area of research driven by concerns about trustworthiness and accountability. Current efforts focus on developing novel explainability tools for various model architectures, including functional random forests, graph neural networks, and deep learning models used in vision and language processing, often employing techniques like partial dependence plots, attention heatmaps, and game-theoretic methods to quantify feature importance and interactions. This research is vital for improving the reliability and interpretability of AI systems across diverse fields, from healthcare and finance to environmental science, ultimately fostering greater trust and responsible AI development.