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Abstract A co-benefit of decarbonization of the power grid is reduced emissions of other air pollutants known to harm human health. However, this co-benefit is typically quantified on an average annual basis, without considering sub-annual dynamics. Here, we investigate how increased penetration of renewable energy could affect the spatial and temporal dynamics of emissions from fossil fuel power plants, as well as associated human health damages. Focusing on the Western United States as a case study, we combine operational grid modeling and air pollution assessment to model changes in precursor emissions and health damages under several renewable energy scaling scenarios. Our findings indicate that as renewable energy penetration increases, average emissions and estimated health damages are reduced. However, emissions and damages on the worst days are significantly slower to improve, largely due to the grid relying on fossil fuels during periods of extreme scarcity. We also observe that at significant (> 6x) scaling of renewable energy generation, the timing of the highest emission days shifts from summer to winter. 

Despite the importance of AI literacy for both children and adults, adults have been understudied. We developed short videos for adults that provided training on the basics of AI understanding, use, and evaluation. In an online experiment, 94 adults aged 30-49 were randomly assigned in a 1:2 ratio to view either short videos on AI history (control group) or AI literacy training videos (treatment group). The results showed that the intervention significantly improved people’s self-efficacy of AI use but not in AI understanding or evaluation. Interestingly, participants’ fears of AI bias, privacy violations, and job replacement increased after the training, although they remained below the midpoints. We argue that the heightened fear in the treatment group reflects a foundation for critical thinking skills, as it moves them closer to a more calibrated, moderate level of fear. Therefore, this study uniquely contributes by utilizing short-form experiential content to both educate and foster a more informed, critical interaction with AI technologies. The implications of designing AI literacy educational materials for adults were discussed. 

Though introduced nearly 50 years ago, the infinitesimal jackknife (IJ) remains a popular modern tool for quantifying predictive uncertainty in complex estimation settings. In particular, when supervised learning ensembles are constructed via bootstrap samples, recent work demonstrated that the IJ estimate of variance is particularly convenient and useful. However, despite the algebraic simplicity of its final form, its derivation is rather complex. As a result, studies clarifying the intuition behind the estimator or rigorously investigating its properties have been severely lacking. This work aims to take a step forward on both fronts. We demonstrate that surprisingly, the exact form of the IJ estimator can be obtained via a straightforward linear regression of the individual bootstrap estimates on their respective weights or via the classical jackknife. The latter realization allows us to formally investigate the bias of the IJ variance estimator and better characterize the settings in which its use is appropriate. Finally, we extend these results to the case of U-statistics where base models are constructed via subsampling rather than bootstrapping and provide a consistent estimate of the resulting variance.