JOURNAL ARTICLE

When data generate populations.

  • Published In: International Journal of Epidemiology, 2024, v. 53, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chiolero, Arnaud; Carmeli, Cristian 3 of 3

Abstract

The article discusses the role of epidemiology in addressing the weaknesses of artificial intelligence (AI) and big data. It emphasizes the importance of causal inference thinking and algorithmic fairness in epidemiology. The article highlights the need to clearly define and identify study and target populations to ensure the external validity of study findings. It also discusses the challenges of using big data, such as the rapidly changing nature of the populations generating the data and the biases that can arise from treating big data as a census. The article suggests that epidemiology has tools to address these issues and make big data useful for improving population health. [Extracted from the article]

Additional Information

  • Source:International Journal of Epidemiology. 2024/02, Vol. 53, Issue 1, p1
  • Document Type:Article
  • Subject Area:Sociology
  • Publication Date:2024
  • ISSN:0300-5771
  • DOI:10.1093/ije/dyad166
  • Accession Number:175392014
  • Copyright Statement:Copyright of International Journal of Epidemiology is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.