Interactive Machine Learning

Interactive Machine Learning (IML) focuses on integrating human expertise into machine learning processes to improve model performance, efficiency, and trustworthiness. Current research emphasizes developing human-AI interaction algorithms, optimizing human-AI team dynamics, and designing effective explanation methods (e.g., data-centric and model-centric explanations) to guide model improvement and build user trust, often leveraging Bayesian Optimization or data augmentation techniques. This field is significant because it addresses the limitations of traditional "black box" machine learning, leading to more robust, explainable, and user-friendly AI systems across diverse applications, including healthcare, command and control, and robotics.