A technique that leverages duplicate records in crowdsourcing data could help to mitigate the effects of biases in research and services that are dependent on government records.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 /Â 30Â days
cancel any time
Subscribe to this journal
Receive 12 digital issues and online access to articles
$99.00 per year
only $8.25 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Liu, Z., Bhandaram, U. & Garg, N. Nat. Comput. Sci. https://doi.org/10.1038/s43588-023-00572-6 (2023).
Lazer, D. et al. Nature 323, 721â723 (2009).
Boyd, D. & Crawford, K. in A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society https://doi.org/10.2139/ssrn.1926431 (SSRN, 2011).
Klinger, D. A. & Bridges, G. S. Criminology 35, 705â726 (1997).
Boeing, G. Environ. Plann. A 52, 449â468 (2020).
Iliadis, A. & Russo, F. Big Data Soc. 3, https://doi.org/10.1177/2053951716674238 (2016).
OâBrien, D. T., Sampson, R. J. & Winship, C. Sociol. Methodol. 45, 101â147 (2015).
OâBrien, D. T. Urban Informatics (CRC Press/Chapman & Hall, 2023).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The author declares no competing interests.
Rights and permissions
About this article
Cite this article
OâBrien, D.T. Disentangling truth from bias in naturally occurring data. Nat Comput Sci 4, 5â6 (2024). https://doi.org/10.1038/s43588-023-00587-z
Published:
Issue Date:
DOI: https://doi.org/10.1038/s43588-023-00587-z