The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Big Data and the danger of being precisely inaccurate
Open accessAbstract
Social scientists and data analysts are increasingly making use of Big Data in their analyses. These data sets are often “found data” arising from purely observational sources rather than data derived under strict rules of a statistically designed experiment. However, since these large data sets easily meet the sample size requirements of most statistical procedures, they give analysts a false sense of security as they proceed to focus on employing traditional statistical methods. We explain how most analyses performed on Big Data today lead to “precisely inaccurate” results that hide biases in the data but are easily overlooked due to the enhanced significance of the results created by the data size. Before any analyses are performed on large data sets, we recommend employing a simple data segmentation technique to control for some major components of observational data biases. These segments will help to improve the accuracy of the results.
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The reason for algorithmic discrimination is related to data selection.
“McFarland and McFarland (2015), Mayson (2018), Raso et al. (2018), Raub (2018), Raghavan et al. (2020), Njoto (2020), Zixun (2020), and Jackson (2021) suggest that the reason for algorithmic discrimination is related to data selection.”
From Ethics and discrimination in artificial intelligence-enabled recruitment practices (2023) · cites this paper as McFarland and McFarland (2015)
Citations
Cites
The Parable of Google Flu: Traps in Big Data Analysis
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014) · Science · doi:10.1126/science.1248506 · Cited by 2,523 · cited 1×
“However, the model began over -reporting flu levels as soon as 2011 – as high as double the number of actual cases (Butler, 2013; Lazer et al., 2014).”
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Research works printed but not linked: 12
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Ethics and discrimination in artificial intelligence-enabled recruitment practices
Chen, Z. (2023) · Humanities and Social Sciences Communications · doi:10.1057/s41599-023-02079-x · Cited by 450
Metadata
- Authors
- McFarland, D. A., & McFarland, H. R.
- Affiliations
- Hearst (United States) (US)
- Corresponding author
- McFarland, Daniel A
- ORCID
- McFarland, Daniel A
- Venue
- Big Data & Society
- Publisher
- SAGE Publications
- Published
- 2015-12-01
- Volume
- 2
- Issue
- 2
- ISSN
- 2053-9517 (ISSN-L)
- OpenAlex
- W2218451112
- Cited by
- 81
- References
- 1
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 4 · 2023: 11 · 2022: 11 · 2021: 16 · 2020: 12 · 2019: 7 · 2018: 4 · 2017: 6 · 2015: 1
- FWCI
- 7.20
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Big Data & Society (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- big data, observational data, data bias, data segmentation, sample size, statistical significance, observational studies, data quality, research accuracy
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