2019 02 News

Our recent work on human-machine collaboration to improve performance of active machine learning has been published in the Human-centric Computing and Information Sciences journal. The work by our team (Legg, Smith, Downing) studies how visual analytics can help inform humans in the development of machine learning models to identify limitations and robustness of models when learning from small limited data samples. We also demonstrate how both humans and machines can inform each other on their levels of confidence when performing such tasks, to suitably account for uncertainty in further decision-making tasks.