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We live in a time of unprecedented global environmental and ecological change: a warming planet, vanishing biodiversity, overfishing and intensifying ecosystem change from fire to draught to invasive species. Not only are these challenges frequently intertwined, all are closely coupled to social, economic, political components which play out in diverse and unequal ways. At the same time, we suddenly have access to ecological and environmental data at a scale we never imagined, thanks to revolutionary technology ranging from satellites to drones to environmental DNA sequencers, opening the door to entirely new approaches for preserving and managing ecosystems and natural resources that are dynamic, highly tuned and spatially varying -- in short, to conservation by algorithm. I will present our work on how algorithmic approaches from artificial intelligence, such as deep reinforcement learning, can allow us to crack some long-standing problems and guide us to some improved ecological outcomes while also highlighting the many open and unsolved challenges. While algorithms which prevent poaching or preserve critical habitat of our non-human co-inhabitants may seem insulated from concerns of equity, racism, and bias so familiar when working with data about humans, such efforts have very real social, economic and political consequences which are easily overlooked, unintended, or undesirable.  I will highlight recent collaborations with social scientists, political ecologists, ethicists and others to illuminate when the hardest question is not can we but should we.