Bias in Facial Classification ML Models
  1. References
  • Abstract
  • 1  Introduction
  • 2  Data
  • 3  Methods
  • 4  Results
  • 5  Conclusions
  • References

References

Buolamwini, Joy. 2023. “Gender Shades: Intersectional Accuracy Disparities in.” MIT Media Lab. https://www.media.mit.edu/publications/gender-shades-intersectional-accuracy-disparities-in-commercial-gender-classification.
Georgetown Law. 2016. “The Perpetual Line-Up: Unregulated Police Face Recognition in America.” Center on Privacy & Technology. https://www.perpetuallineup.org.
Huilgol, Purva. 2021. “Accuracy vs. F1-Score - Analytics Vidhya - Medium.” Medium, December. https://medium.com/analytics-vidhya/accuracy-vs-f1-score-6258237beca2.
Karkkainen, Kimmo, and Jungseock Joo. 2021. “FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation.” In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 1548–58.
Lohr, Steve. 2018. “Facial Recognition Is Accurate, if You’re a White Guy.” N.Y. Times, February. https://www.nytimes.com/2018/02/09/technology/facial-recognition-race-artificial-intelligence.html.
NIST. 2020. “NIST Study Evaluates Effects of Race, Age, Sex on Face Recognition Software | NIST.” NIST. https://www.nist.gov/news-events/news/2019/12/nist-study-evaluates-effects-race-age-sex-face-recognition-software.
Serengil, Sefik Ilkin, and Alper Ozpinar. 2021. “HyperExtended LightFace: A Facial Attribute Analysis Framework.” In 2021 International Conference on Engineering and Emerging Technologies (ICEET), 1–4. IEEE. https://doi.org/10.1109/ICEET53442.2021.9659697.
“UTKFace.” 2021. UTKFace. https://susanqq.github.io/UTKFace.
5  Conclusions