From theory to practice: Harmonizing taxonomies of trustworthy AI.

The increasing capabilities of AI pose new risks and vulnerabilities for organizations and decision makers. Several trustworthy AI frameworks have been created by U.S.

Information, partisanship, and preferences in a pandemic.

We investigate the role of information exposure in shaping attitudes and behaviors related to the SARS-CoV-2 (COVID-19) pandemic and whether baseline political affiliation and news diet mediate effects. In December 2020, we randomly assigned 5,009 U.S.

The effects of department of Veterans Affairs medical centers on socio-economic outcomes: Evidence from the Paycheck Protection Program.

Do medical facilities also help advance improvements in socio-economic outcomes? We focus on Veterans, a vulnerable group over the COVID-19 pandemic who have access to a comprehensive healthcare network, and the receipt of funds from the Paycheck Protection Program (PPP) between April and June as a source of variation. First, we find that Veterans received 3.5% more loans and 6.

Did the paycheck protection program hit the target?

This paper provides a comprehensive assessment of financial intermediation and the economic effects of the Paycheck Protection Program (PPP), a large and novel small business support program that was part of the initial policy response to the COVID-19 pandemic in the US. We use loan-level microdata for all PPP loans and high-frequency administrative employment data to present three main findings. First, banks played an important role in mediating program targeting, which helps explain why some funds initially flowed to regions that were less adversely affected by the pandemic.

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration.

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation.

Sectoral digital intensity and GDP growth after a large employment shock: A simple extrapolation exercise.

We introduce a state-dependent algorithm with minimal data requirements for predicting output dynamics as a function of employment across industries and locations. The method generalizes insights of Okun (1963) by leveraging measures of industry heterogeneity. We use the algorithm to examine gross domestic product (GDP) dynamics following the COVID-19 pandemic of 2020, delivering informative projections of aggregate and sectoral output.

Does marriage protect mental health? Evidence from the COVID-19 pandemic.

Using weekly variation from April 23 to June 23 2020, we exploit the surge in unemployment over the coronavirus pandemic to identify the effects on mental health outcomes and the role of marital status as a protective factor for households. We find that married respondents are 1-2 percentage points less likely, relative to their unmarried counterparts, to experience mental health problems following declines in work-related income since the start of the pandemic. Our results suggest that the combination of intrafamily substitution and the psychological benefits of marriage helps insure against unanticipated fluctuations in job and income loss.

Correction: How social capital helps communities weather the COVID-19 pandemic.

[This corrects the article DOI: 10.1371/journal.pone.

Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs.

Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. First, using comprehensive data on over 10 000 Veterans' medical history, demographics and lab results, we estimate five AI models. Our XGBoost model performs the best, producing an area under the receive operator characteristics curve (AUROC) and area under the precision-recall curve of 0.

Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans.

Background: Despite widespread agreement that artificial intelligence (AI) offers significant benefits for individuals and society at large, there are also serious challenges to overcome with respect to its governance. Recent policymaking has focused on establishing principles for the trustworthy use of AI. Adhering to these principles is especially important for ensuring that the development and application of AI raises economic and social welfare, including among vulnerable groups and veterans.

The impact of COVID-19 on the U.S. child care market: Evidence from stay-at-home orders.

Stay-at-home orders (SAHOs) were implemented in most U.S. states to mitigate the spread of COVID-19.

Leveraging machine learning to characterize the role of socio-economic determinants on physical health and well-being among veterans.

Introduction: We investigate the contribution of demographic, socio-economic, and geographic characteristics as determinants of physical health and well-being to guide public health policies and preventative behavior interventions (e.g., countering coronavirus).

Pandemic response policies' democratizing effects on online learning.

The COVID-19 pandemic has changed peoples' lives in unexpected ways, especially how they allocate their time between work and other activities. Demand for online learning has surged during a period of mass layoffs and transition to remote work and schooling. Can this uptake in online learning help close longstanding skills gaps in the US workforce in a sustainable and equitable manner? We answer this question by analyzing individual engagement data of DataCamp users between October 2019 and September 2020 ( = 277,425).

How Much Does the (Social) Environment Matter? Using Artificial Intelligence to Predict COVID-19 Outcomes with Socio-demographic Data.

While the coronavirus pandemic has affected all demographic brackets and geographies, certain areas have been more adversely affected than others. This paper focuses on Veterans as a potentially vulnerable group that might be systematically more exposed to infection than others because of their co-morbidities, i.e.

How social capital helps communities weather the COVID-19 pandemic.

Why have the effects of COVID-19 been so unevenly geographically distributed in the United States? This paper investigates the role of social capital as a mediating factor for the spread of the virus. Because social capital is associated with greater trust and relationships within a community, it could endow individuals with a greater concern for others, thereby leading to more hygienic practices and social distancing. Using data for over 2,700 US counties, we investigate how social capital explains the level and growth rate of infections.

Human flourishing and religious liberty: Evidence from over 150 countries.

This paper studies the spatial and time series patterns of religious liberty across countries and estimates its effect on measures of human flourishing. First, while there are significant cross-country differences in religious liberty, it has declined in the past decade across countries, particularly among countries that rank higher in economic freedom. Second, countries with greater religious liberty nonetheless exhibit greater levels of economic freedom, particularly property rights.

Understanding the impact of COVID-19 intervention policies on the hospitality labor market.

Using new high-frequency data that covers a representative sample of small businesses in the United States, this study investigates the effects of the COVID-19 pandemic and the resulting state policies on the hospitality industry. First, business closure policies are associated with a 20-30% reduction of non-salaried workers in the food/drink and leisure/entertainment sectors during March-April of 2020. Second, business reopening policies play a statistically significant role in slowly reviving the labor market.

Converging technologies: a critical analysis of cognitive enhancement for public policy application.

This paper investigates cognitive enhancement, specifically biological cognitive enhancement (BCE), as a converging technology, and its implications for public policy. With an increasing rate of technological advancements, the legal, social, and economic frameworks lag behind the scientific advancements that they support. This lag poses significant challenges for policymakers if it is not dealt with sufficiently within the right analytical context.