Understanding and combatting misinformation across 16 countries on six continents.

The spread of misinformation online is a global problem that requires global solutions. To that end, we conducted an experiment in 16 countries across 6 continents (N = 34,286; 676,605 observations) to investigate predictors of susceptibility to misinformation about COVID-19, and interventions to combat the spread of this misinformation. In every country, participants with a more analytic cognitive style and stronger accuracy-related motivations were better at discerning truth from falsehood; valuing democracy was also associated with greater truth discernment, whereas endorsement of individual responsibility over government support was negatively associated with truth discernment in most countries.

Art and the science of generative AI.

Understanding shifts in creative work will help guide AI's impact on the media ecosystem.

The social media context interferes with truth discernment.

There is widespread concern about misinformation circulating on social media. In particular, many argue that the context of social media itself may make people susceptible to the influence of false claims. Here, we test that claim by asking whether simply considering sharing news on social media reduces the extent to which people discriminate truth from falsehood when judging accuracy.

Deepfake detection by human crowds, machines, and machine-informed crowds.

The recent emergence of machine-manipulated media raises an important societal question: How can we know whether a video that we watch is real or fake? In two online studies with 15,016 participants, we present authentic videos and deepfakes and ask participants to identify which is which. We compare the performance of ordinary human observers with the leading computer vision deepfake detection model and find them similarly accurate, while making different kinds of mistakes. Together, participants with access to the model's prediction are more accurate than either alone, but inaccurate model predictions often decrease participants' accuracy.

Shifting attention to accuracy can reduce misinformation online.

In recent years, there has been a great deal of concern about the proliferation of false and misleading news on social media. Academics and practitioners alike have asked why people share such misinformation, and sought solutions to reduce the sharing of misinformation. Here, we attempt to address both of these questions.

Who Gets Credit for AI-Generated Art?

The recent sale of an artificial intelligence (AI)-generated portrait for $432,000 at Christie's art auction has raised questions about how credit and responsibility should be allocated to individuals involved and how the anthropomorphic perception of the AI system contributed to the artwork's success. Here, we identify natural heterogeneity in the extent to which different people perceive AI as anthropomorphic. We find that differences in the perception of AI anthropomorphicity are associated with different allocations of responsibility to the AI system and credit to different stakeholders involved in art production.

Using clinical data to predict high-cost performance coding issues associated with pressure ulcers: a multilevel cohort model.

Objective: Hospital-acquired pressure ulcers (HAPUs) have a mortality rate of 11.6%, are costly to treat, and result in Medicare reimbursement penalties. Medicare codes HAPUs according to Agency for Healthcare Research and Quality Patient-Safety Indicator 3 (PSI-03), but they are sometimes inappropriately coded.

Risking your life without a second thought: intuitive decision-making and extreme altruism.

When faced with the chance to help someone in mortal danger, what is our first response? Do we leap into action, only later considering the risks to ourselves? Or must instinctive self-preservation be overcome by will-power in order to act? We investigate this question by examining the testimony of Carnegie Hero Medal Recipients (CHMRs), extreme altruists who risked their lives to save others. We collected published interviews with CHMRs where they described their decisions to help. We then had participants rate the intuitiveness versus deliberativeness of the decision-making process described in each CHMR statement.