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. Subtly prompting people to think about accuracy had a generally positive effect on the veracity of news that people were willing to share across countries, as did minimal digital literacy tips. Finally, aggregating the ratings of our non-expert participants was able to differentiate true from false headlines with high accuracy in all countries via the 'wisdom of crowds'. The consistent patterns we observe suggest that the psychological factors underlying the misinformation challenge are similar across different regional settings, and that similar solutions may be broadly effective.

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. In a large online experiment examining coronavirus disease 2019 (COVID-19) and political news (N = 3157 Americans), we find support for this possibility. When judging the accuracy of headlines, participants were worse at discerning truth from falsehood if they both evaluated accuracy and indicated their sharing intentions, compared to just evaluating accuracy. These results suggest that people may be particularly vulnerable to believing false claims on social media, given that sharing is a core element of what makes social media "social."

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. To probe the relative strengths and weaknesses of humans and machines as detectors of deepfakes, we examine human and machine performance across video-level features, and we evaluate the impact of preregistered randomized interventions on deepfake detection. We find that manipulations designed to disrupt visual processing of faces hinder human participants' performance while mostly not affecting the model's performance, suggesting a role for specialized cognitive capacities in explaining human deepfake detection performance.

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 media1-4. Academics and practitioners alike have asked why people share such misinformation, and sought solutions to reduce the sharing of misinformation5-7. Here, we attempt to address both of these questions. First, we find that the veracity of headlines has little effect on sharing intentions, despite having a large effect on judgments of accuracy. This dissociation suggests that sharing does not necessarily indicate belief. Nonetheless, most participants say it is important to share only accurate news. To shed light on this apparent contradiction, we carried out four survey experiments and a field experiment on Twitter; the results show that subtly shifting attention to accuracy increases the quality of news that people subsequently share. Together with additional computational analyses, these findings indicate that people often share misinformation because their attention is focused on factors other than accuracy-and therefore they fail to implement a strongly held preference for accurate sharing. Our results challenge the popular claim that people value partisanship over accuracy8,9, and provide evidence for scalable attention-based interventions that social media platforms could easily implement to counter misinformation online.

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. We then show that perceptions of AI anthropomorphicity can be manipulated by changing the language used to talk about AI-as a tool versus agent-with consequences for artists and AI practitioners. Our findings shed light on what is at stake when we anthropomorphize AI systems and offer an empirical lens to reason about how to allocate credit and responsibility to human stakeholders.

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. The objective is to use electronic health records to predict pressure ulcers and to identify coding issues leading to penalties. MATERIALS AND METHODS: We evaluated all hospitalized patient electronic medical records at an academic medical center data repository between 2011 and 2014. These data contained patient encounter level demographic variables, diagnoses, prescription drugs, and provider orders. HAPUs were defined by PSI-03: stages III, IV, or unstageable pressure ulcers not present on admission as a secondary diagnosis, excluding cases of paralysis. Random forests reduced data dimensionality. Multilevel logistic regression of patient encounters evaluated associations between covariates and HAPU incidence. RESULTS: The approach produced a sample population of 21 153 patients with 1549 PSI-03 cases. The greatest odds ratio (OR) of HAPU incidence was among patients diagnosed with spinal cord injury (ICD-9 907.2: OR = 14.3; P < .001), and 71% of spinal cord injuries were not properly coded for paralysis, leading to a PSI-03 flag. Other high ORs included bed confinement (ICD-9 V49.84: OR = 3.1, P < .001) and provider-ordered pre-albumin lab (OR = 2.5, P < .001). DISCUSSION: This analysis identifies spinal cord injuries as high risk for HAPUs and as being often inappropriately coded without paralysis, leading to PSI-03 flags. The resulting statistical model can be tested to predict HAPUs during hospitalization. CONCLUSION: Inappropriate coding of conditions leads to poor hospital performance measures and Medicare reimbursement penalties.

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. The statements were judged to be overwhelmingly dominated by intuition; to be significantly more intuitive than a set of control statements describing deliberative decision-making; and to not differ significantly from a set of intuitive control statements. This remained true when restricting to scenarios in which the CHMRs had sufficient time to reflect before acting if they had so chosen. Text-analysis software found similar results. These findings suggest that high-stakes extreme altruism may be largely motivated by automatic, intuitive processes.