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Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence—a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.

Collective intelligence has emerged as a powerful mechanism to boost decision accuracy across many domains, such as geopolitical forecasting, investment, and medical diagnostics. However, collective intelligence has been mostly applied to relatively simple decision tasks (e.g., binary classifications). Applications in more open-ended tasks with a much larger problem space, such as emergency management or general medical diagnostics, are largely lacking, due to the challenge of integrating unstandardized inputs from different crowd members. Here we present a fully-automated approach for harnessing collective intelligence in the domain of general medical diagnostics. Our approach leverages semantic knowledge graphs, natural language processing, and the SNOMED CT medical ontology to overcome a major hurdle to collective intelligence in open-ended medical diagnostics, namely to identify the intended diagnosis from unstructured text. We tested our method on 1,333 medical cases diagnosed on a medical crowdsourcing platform: The Human Diagnosis Project. Each case was independently rated by ten diagnosticians. Comparing the diagnostic accuracy of single diagnosticians with the collective diagnosis of differently-sized groups, we find that our method substantially increases diagnostic accuracy: while single diagnosticians achieved 46\% accuracy, pooling the decisions of ten diagnosticians increased this to 76\%. Improvements occurred across medical specialties, chief complaints, and diagnosticians' tenure levels. Our results show the life-saving potential of tapping into the collective intelligence of the global medical community to reduce diagnostic errors and increase patient safety.

Many parts of our social lives are speeding up, a process known as social acceleration. How social acceleration impacts people’s ability to judge the veracity of online news, and ultimately the spread of misinformation, is largely unknown. We examined the effects of accelerated online dynamics, operationalised as time pressure, on online misinformation evaluation. Participants judged the veracity of true and false news headlines with or without time pressure. We used signal detection theory to disentangle the effects of time pressure on discrimination ability and response bias, as well as on four key determinants of misinformation susceptibility: analytical thinking, ideological congruency, motivated reflection, and familiarity. Time pressure reduced participants’ ability to accurately distinguish true from false news (discrimination ability) but did not alter their tendency to classify an item as true or false (response bias). Key drivers of misinformation susceptibility, such as ideological congruency and familiarity, remained influential under time pressure. Our results highlight the dangers of social acceleration online: People are less able to accurately judge the veracity of news online, while prominent drivers of misinformation susceptibility remain present. Interventions aimed at increasing deliberation may thus be fruitful avenues to combat online misinformation.

Inequalities and injustices are thorny issues in liberal societies, manifesting in forms such as the gender pay gap; sentencing discrepancies among Black, Hispanic, and White defendants; and unequal medical resource distribution across ethnicities. One cause of these inequalities is implicit social bias: unconsciously formed associations between social groups and attributions such as "nurturing," "lazy," or "uneducated." One strategy to counteract implicit and explicit human biases is delegating crucial decisions, such as how to allocate benefits, resources, or opportunities, to algorithms. Algorithms, however, are not necessarily impartial and objective. Although they can detect and mitigate human biases, they can also perpetuate and even amplify existing inequalities and injustices. We explore how a philosophical thought experiment, Rawls's veil of ignorance, and a psychological phenomenon, deliberate ignorance, can help shield individuals, institutions, and algorithms from biases. We discuss the benefits and drawbacks of methods for shielding human and artificial decision-makers from potentially biasing information. We then broaden our discussion beyond the issues of bias and fairness and turn to a research agenda aimed at improving human judgment accuracy with the assistance of algorithms that conceal information that has the potential to undermine performance. Finally, we propose interdisciplinary research questions.

The use of information technologies for the public interest, such as COVID-19 tracking apps that aim to reduce the spread of COVID-19 during the pandemic, involve a dilemma between public interest benefits and privacy concerns. Critical in resolving this conflict of interest are citizens’ trust in the government and the risks posed by COVID-19. How much can the government be trusted to access private information? Furthermore, to what extend do the health benefits posed by the technology outweigh the personal risks to one's privacy? We hypothesize that citizens’ acceptance of the technology can be conceptualized as a calculus of privacy concerns, government trust, and the public benefit of adopting a potentially privacy-encroaching technology. The importance that citizens place on their privacy and the extent to which they trust their governments vary though out the world. The present study examined the public’s privacy calculus across nine countries (Australia, Germany, Italy, Japan, Spain, Switzerland, Taiwan, the United Kingdom, and the United States) focusing on social acceptance of contact-tracing technologies during the COVID-19 pandemic. We found that across countries, privacy concerns were negatively associated with citizens’ acceptance of the technology, while government trust, perceived effectiveness of the technology, and the health threats of COVID-19 were positively associated. National cultural orientations moderate the effects of the basic factors of privacy calculus. In particular, individualism (value of the individual) amplified the effect of privacy concerns, whereas general trust (trust in the wider public) amplified the effect of government trust. National culture therefore requires careful attention in resolving public policy dilemmas of privacy, trust, and public interest.

Although science-related misconceptions such as climate denial are often held with high confidence, research explaining the origin of erroneous beliefs has historically remained largely detached from research into metacognition—the insight and confidence we have in these beliefs. Instead, traditional explanations were based on political and econom5ic worldviews. Here, we provide a joint investigation into the explanatory value of ideology-driven cultural cognition, and more reflective, metacognitive accounts for belief-updating and information sharing about contested science. We presented a national sample of citizens in Germany with noisy information about climate change, most of it aligned with scientific consensus, but some contradicting consensus. Frequentist and Bayesian results demonstrate that, skeptical belief-updating was more pronounced among citizens with lower metacognitive insight; and sharing claims about the uncertainty of climate science was increased among citizens with lower metacognitive confidence in climate change knowledge--even when controlling for their worldviews. Furthermore, the predictivity of metacognitive variables was systematically reduced among climate skeptic citizens, revealing a striking interaction between worldviews and the reliance on more reflective, metacognitive cues for belief-updating. Together these findings suggest that both worldview-related, and more reflective, metacognitive accounts—and their interactions—explain beliefs about contested issues; advancing more realistic theories of the “ideology” vs. “rationality” of science beliefs.