 However, the new capabilities of deep learning systems have brought with them new concerns. Deep networks typically learn from vastly more data than their predecessors (AlphaZero learned from over 100 million self-played Go games), and can extract much more subtle, structured patterns. While the analysis of AlphaZero’s unusual approach to strategy has created a mini-revolution in the study of chess and Go (Sadler &amp; Regan 2019), it also raised concerns that the solutions deep networks discover are alien and mysterious. It is natural, therefore, to have second thoughts about depending on deep learning technologies for tasks that must be responsive to human interests and goals.