Companies have considered adoption of various high-level artificial intelligence (AI) principles for responsible AI, but there is less clarity on how to implement these principles as organizational practices. This paper reviews the principles-to-practices gap. We outline five explanations for this gap ranging from a disciplinary divide to an overabundance of tools. In turn, we argue that an impact assessment framework which is broad, operationalizable, flexible, iterative, guided, and participatory is a promising approach to close the principles-to-practices gap. Finally, to help practitioners with applying these recommendations, we review a case study of AI's use in forest ecosystem restoration, demonstrating how an impact assessment framework can translate into effective and responsible AI practices.

By 2025 the aim is for 3.5 million tons more of "permanent, quantifiable greenhouse gas reductions." That would be lot of cuts--akin to the tailpipe output of 2.6 million passenger cars. One of the best spots to reduce emissions is right in BP's oil and gas fields. BP figures that half of its fugitive methane emissions--a fancy way of saying natural gas leaking out of pumps and pipes--come from its operations in the Lower 48. And a good portion of those happen in mature fields like the one near Wamsutter, in the Great Divide Basin of Wyoming.

The Intergovernmental Panel on Climate Change (IPCC) highlights six main lines of evidence for climate change. First, we have tracked (see chart) the unprecedented recent increase in the amount of atmospheric carbon dioxide and other greenhouse gases since the beginning of the industrial revolution. By burning coal, oil, and natural gas, we accelerate the process, releasing vast amounts of carbon (carbon that took millions of years to accumulate) into the atmosphere every year.