Model Shift

Model shift, encompassing changes in model parameters or data distributions between training and deployment, is a critical challenge in machine learning, impacting model robustness and reliability. Current research focuses on developing methods to quantify and mitigate the effects of model shift, including probabilistic guarantees for robust counterfactual explanations and algorithms for adapting models to new data distributions, often employing techniques like model splitting and transfer learning. Addressing model shift is crucial for building trustworthy and dependable AI systems across diverse applications, improving the accuracy and interpretability of predictions in real-world scenarios.