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Conservatism predicts aversion to consequential Artificial Intelligence.

ABSTRACT: Artificial intelligence (AI) has the potential to revolutionize society by automating tasks as diverse as driving cars, diagnosing diseases, and providing legal advice. The degree to which AI can improve outcomes in these and other domains depends on how comfortable people are trusting AI for these tasks, which in turn depends on lay perceptions of AI. The present research examines how these critical lay perceptions may vary as a function of conservatism. Using five survey experiments, we find that political conservatism is associated with low comfort with and trust in AI-i.e., with AI aversion. This relationship between conservatism and AI aversion is explained by the link between conservatism and risk perception; more conservative individuals perceive AI as being riskier and are therefore more averse to its adoption. Finally, we test whether a moral reframing intervention can reduce AI aversion among conservatives.

SUBMITTER: Castelo N

PROVIDER: S-EPMC8687590 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Conservatism predicts aversion to consequential Artificial Intelligence.

Castelo Noah N Ward Adrian F AF

PloS one 20211220 12

Artificial intelligence (AI) has the potential to revolutionize society by automating tasks as diverse as driving cars, diagnosing diseases, and providing legal advice. The degree to which AI can improve outcomes in these and other domains depends on how comfortable people are trusting AI for these tasks, which in turn depends on lay perceptions of AI. The present research examines how these critical lay perceptions may vary as a function of conservatism. Using five survey experiments, we find t ...[more]

PMID: 34928989

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Project description:BACKGROUND: The ejection fraction (EF) provides critical information about heart failure (HF) and its management. Electrocardiography (ECG) is a noninvasive screening tool for cardiac electrophysiological activities that has been used to detect patients with low EF based on a deep learning model (DLM) trained via large amounts of data. However, no studies have widely investigated its clinical impacts. OBJECTIVE: This study developed a DLM to estimate EF via ECG (ECG-EF). We further investigated the relationship between ECG-EF and echo-based EF (ECHO-EF) and explored their contributions to future cardiovascular adverse events. METHODS: There were 57,206 ECGs with corresponding echocardiograms used to train our DLM. We compared a series of training strategies and selected the best DLM. The architecture of the DLM was based on ECG12Net, developed previously. Next, 10,762 ECGs were used for validation, and another 20,629 ECGs were employed to conduct the accuracy test. The changes between ECG-EF and ECHO-EF were evaluated. The primary follow-up adverse events included future ECHO-EF changes and major adverse cardiovascular events (MACEs). RESULTS: The sex-/age-matching strategy-trained DLM achieved the best area under the curve (AUC) of 0.9472 with a sensitivity of 86.9% and specificity of 89.6% in the follow-up cohort, with a correlation of 0.603 and a mean absolute error of 7.436. In patients with accurate prediction (initial difference < 10%), the change traces of ECG-EF and ECHO-EF were more consistent (R-square = 0.351) than in all patients (R-square = 0.115). Patients with lower ECG-EF (≤35%) exhibited a greater risk of cardiovascular (CV) complications, delayed ECHO-EF recovery, and earlier ECHO-EF deterioration than patients with normal ECG-EF (>50%). Importantly, ECG-EF demonstrated an independent impact on MACEs and all CV adverse outcomes, with better prediction of CV outcomes than ECHO-EF. CONCLUSIONS: The ECG-EF could be used to initially screen asymptomatic left ventricular dysfunction (LVD) and it could also independently contribute to the predictions of future CV adverse events. Although further large-scale studies are warranted, DLM-based ECG-EF could serve as a promising diagnostic supportive and management-guided tool for CV disease prediction and the care of patients with LVD.

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Conservatism predicts aversion to consequential Artificial Intelligence.

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Conservatism predicts aversion to consequential Artificial Intelligence.

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