An important under-explored issue within energy systems literature is understanding the challenges that arise when two energy and environment goals conflict with one another. In particular, there is a desire to install more large-scale renewable energy generation resources to decarbonize the electricity sector, yet the siting of these power sources is increasingly occurring on sensitive and vulnerable lands that often serve as critical habitats for endangered species. This grant led by Rebecca Hernandez at the University of California, Davis will explore the challenges that arise when these two energy and environment goals conflict. Grant funds will allow Hernandez to explore this “renewable energy vs. conservation” tradeoff, exploring the West and Southwest regions of the United States to map and model the overlap between current and future conservation needs and renewable energy installation locations. The team will also engage a range of stakeholders to identify community preferences associated with these tradeoffs, with a particular focus on Indigenous nations and tribal communities. Funds will allow Hernandez to produce at least 4 peer-reviewed articles examining the conflict associated with species threatened by climate change and renewable energy development; produce detailed maps of range shift patterns for current and future renewable energy impacts on endangered species; undertake 2 stakeholder workshops; train 1 graduate student and 1 postdoctoral fellow; and produce a publicly accessible Climate-Smart Siting Guide for public distributions.

Recent evidence and trends have been undermining some predictions that robots are about to steal all our jobs. Researchers have evolved from believing that automation must lead to substantial unemployment. Many now argue that automation may actually increase employment. This can happen because Artificial Intellignece (AI) raises firm productivity and also because, in some situations, AI acts as a complement to human expertise. Rather than having nothing to do, it looks like we may instead learn to work alongside our new robotic friends. This grant supports Nikhil Agarwal and Tobias Salz at MIT who are investigating the collaborative nature of interactions between people and AI in knowledge-intensive environments. Their goal is to understand better how human decision-makers combine their own contextual information or intuition with machine generated predictions.   Grant funds will allow Agarwal and Salz to develop theoretical models of human decision-making with and without AI assistance, then test these models by running experiments on how human experts actually make use of AI tools in practice. The team will initially test their models through observing how radiologists interpret patients’ chest X-rays, varying the availability and timing of AI predictions and the presence or absence of contextual data such as the patients’ clinical histories. This will allow the team to explore, to take one example, the weight that radiologists give to AI predictions under different circumstances. The findings of this project, however, will have implications that go far beyond the practice of radiology, including potential further applications concerning the use of AI in financial transactions, corporate operations, and risk assessments.