RESUMO
Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists' visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community's ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.
Assuntos
Inteligência Artificial , Ilusões , Conhecimento , Projetos de Pesquisa , Pesquisadores , Humanos , Inteligência Artificial/provisão & distribuição , Inteligência Artificial/tendências , Cognição , Difusão de Inovações , Eficiência , Reprodutibilidade dos Testes , Projetos de Pesquisa/normas , Projetos de Pesquisa/tendências , Risco , Pesquisadores/psicologia , Pesquisadores/normasRESUMO
O aumento significativo de casos de dengue registrado nas primeiras semanas de 2024 no Brasil tem despertado a atenção de especialistas. O pesquisador e infectologista do Instituto Nacional de Infectologia (INI/Fiocruz), André Siqueira, destaca que, diante dessa realidade, é crucial abordar de maneira eficaz tanto o tratamento quanto a prevenção da doença.
