Investigating bias in deep face analysis: The KANFace dataset and empirical study
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Generation of synthetic data by scaling, rotating, and orientation to meet training sample requirements leads to biased results due to less diversified datasets.
“The generation of synthetic data by scaling, rotating, and orientation to meet the requirement of the training sample data leads to biased results due to less diversified datasets [48].”
From Artificial intelligence bias in medical system designs: a systematic review (2023) · cites this paper as [48]
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- Authors
- Georgopoulos, M., Panagakis, Y., & Pantic, M.
- Affiliations
- Imperial College London (GB)
- National and Kapodistrian University of Athens (GR)
- Corresponding author
- Georgopoulos, Markos
- Venue
- Image and Vision Computing
- Publisher
- Elsevier BV
- Published
- 2020-10
- Volume
- 102
- Pages
- 103954
- ISSN
- 0262-8856 (ISSN-L)
- OpenAlex
- W3024292796
- Cited by
- 42
- References
- 56
- Citations by year
- 2025: 10 · 2024: 6 · 2023: 8 · 2022: 6 · 2021: 7 · 2020: 5
- FWCI
- 2.53
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Image and Vision Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Engineering and Physical Sciences Research Council
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face recognition and analysis
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Face and Expression Recognition
- Keywords
- demographic bias, face recognition, age estimation, gender classification, kinship verification, algorithmic fairness
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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