As machine learning (ML) capabilities advance, and with the advent of widely available low-cost cloud computing, AI will inevitably be applied to a wider range of more challenging problems, including those that affect the outcomes for millions of individuals throughout society. In high impact, complex settings, it simply isn’t realistic to train a model up front with a single batch of training data and expect it to perform well in all possible scenarios - such a naïve approach will almost certainly fail to capture some of the underlying nuance and edge cases of the situation, leaving gaps in performance and risk of failure during use. Active learning provides a promising way around the issue, empowering the AI to learn from human teachers in uncertain or novel settings and on new data. This architecture allows human experts to impart knowledge gradually as and when they become aware of AI shortcomings, improving performance through teaching and demonstration.
