Version 1
: Received: 25 March 2024 / Approved: 25 March 2024 / Online: 26 March 2024 (08:15:07 CET)
How to cite:
Panić, N.; Marjanović, M.; Bezdan, T. Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models. Preprints2024, 2024031497. https://doi.org/10.20944/preprints202403.1497.v1
Panić, N.; Marjanović, M.; Bezdan, T. Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models. Preprints 2024, 2024031497. https://doi.org/10.20944/preprints202403.1497.v1
Panić, N.; Marjanović, M.; Bezdan, T. Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models. Preprints2024, 2024031497. https://doi.org/10.20944/preprints202403.1497.v1
APA Style
Panić, N., Marjanović, M., & Bezdan, T. (2024). Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models. Preprints. https://doi.org/10.20944/preprints202403.1497.v1
Chicago/Turabian Style
Panić, N., Marina Marjanović and Timea Bezdan. 2024 "Ethnic Representation Matters: Investigating Bias in Facial Age Prediction Models" Preprints. https://doi.org/10.20944/preprints202403.1497.v1
Abstract
In the context of artificial intelligence's pervasive integration across sectors, the rigorous examination of methodologies used in constructing mathematical models is imperative. This study delves into facial recognition models, particularly in their ability to extract features such as age. We address the prevalent practice of training models with unbalanced datasets and its implications for model performance and bias introduction. Utilizing 27,305 facial images, we investigate the influence of dataset imbalance on age prediction based on ethnicity. Employing preprocessing and oversampling techniques, we equalize sample sizes across ethnicities. A Convolutional Neural Network trained on the balanced dataset serves as our baseline, while three additional models undergo training with a 50% sample size reduction for each group to assess accuracy degradation. Our findings reveal a notable decline in accuracy for ethnic groups with reduced representation. The Asian group experiences a 67.94% accuracy deterioration, followed by a 51.60% drop for the Black group, and a 38.46% decline for the White group. These results underscore the sensitivity of certain ethnic groups to underrepresentation, highlighting the nuanced impact on age prediction accuracy. While a 50% reduction in representation does not uniformly result in a 50% accuracy decline, it unquestionably influences predictive accuracy for affected groups.
Keywords
Facial recognition; Age prediction; Convolutional Neural Network (CNN); Ethnicity bias
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
