Calls for more broad-based, integrated, useful knowledge now abound in the world of global environmental change science. They evidence many scientists’ desire to help humanity confront the momentous biophysical implications of its own actions. But they also reveal a limited conception of social science and virtually ignore the humanities. They thereby endorse a stunted conception of ‘human dimensions’ at a time when the challenges posed by global environmental change are increasing in magnitude, scale and scope. Here, we make the case for a richer conception predicated on broader intellectual engagement and identify some preconditions for its practical fulfilment. Interdisciplinary dialogue, we suggest, should engender plural representations of Earth’s present and future that are reflective of divergent human values and aspirations. In turn, this might insure publics and decision-makers against overly narrow conceptions of what is possible and desirable as they consider the profound questions raised by global environmental change

Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

Neural networks are among the most accurate supervised learning methods in use today. However, their opacity makes them difficult to trust in critical applications, especially if conditions in training may differ from those in test. Recent work on explanations for black-box models has produced tools (e.g. LIME) to show the implicit rules behind predictions. These tools can help us identify when models are right for the wrong reasons. However, these methods do not scale to explaining entire datasets and cannot correct the problems they reveal. We introduce a method for efficiently explaining and regularizing differentiable models by examining and selectively penalizing their input gradients. We apply these penalties both based on expert annotation and in an unsupervised fashion that produces multiple classifiers with qualitatively different decision boundaries. On multiple datasets, we show our approach generates faithful explanations and models that generalize much better when conditions differ between training and test.

Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help. Here we describe how machine learning can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by machine learning, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the machine learning community to join the global effort against climate change.