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Libertarian paternalists argue that psychological research has shown that intuition is systematically flawed and we are hardly educable because our cognitive biases resemble stable visual illusions. Thus, they maintain, authorities who know what is best for us need to step in and steer our behavior with the help of nudges. Nudges are nothing new; justifying them on the basis of a latent irrationality is. Technological paternalism is government by algorithms, with tech companies and state governments using digital technology to predict and control citizens’ behavior. This philosophy claims first that AI is, or soon will be, superior to human intuition in all respects; second, people should defer to algorithms’ recommendations. I contend that algorithms and big data can outperform humans in tasks that are well-defined and stable, e.g., playing chess and working on assembly lines, but not in ill-defined and unstable tasks, e.g., finding the best mate and predicting human behavior. Misleadingly, the “dataist” worldview promotes algorithms as if these were omniscient beings and so people should allow them to decide for the good of each what job to accept, whom to marry, and whom to vote for.
What is critical thinking? To paraphrase the Enlightenment philosopher Immanuel Kant, it is the emergence from one’s self-imposed nonage. Nonage is the inability to use one’s mind without another’s guidance. This inability is self-imposed if its cause lies not in the limits of one’s mind but in the lack of courage to use it independently, without others’ guidance. Yet, in the age of powerful algorithms that play better chess and Go than humans, recommend the music and books we like, predict criminal behavior, and even find us the ideal romantic partner, why would we still need to think critically? Would it not be more economical to cease wasting time on thinking and reflecting, and just click and like? I argue that we need more, not less, critical thinking in the digital age. I discuss several tools for critical thinking, including asking the right questions and detecting misleading statistics, and illustrate these by online dating sites, HIV tests, cancer diagnosis, big data predictive analytics, the Social Credit System, and more. Advances in technology require risk-literate people who can control digital media rather be controlled by it.
We evaluated syndromic indicators of influenza disease activity developed using emergency department (ED) data – total ED visits attributed to influenza-like illness (ILI) (‘ED ILI volume’) and percentage of visits attributed to ILI (‘ED ILI percent’) – and Google flu trends (GFT) data (ILI cases/100 000 physician visits). Congruity and correlation among these indicators and between these indicators and weekly count of laboratory-confirmed influenza in Manitoba was assessed graphically using linear regression models. Both ED and GFT data performed well as syndromic indicators of influenza activity, and were highly correlated with each other in real time. The strongest correlations between virological data and ED ILI volume and ED ILI percent, respectively, were 0·77 and 0·71. The strongest correlation of GFT was 0·74. Seasonal influenza activity may be effectively monitored using ED and GFT data.
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