Assessing Intersectional Bias in Representations of Pre-Trained Image Recognition Models
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913880681218048 |
|---|---|
| author | Krug, Valerie Stober, Sebastian |
| author_facet | Krug, Valerie Stober, Sebastian |
| contents | Deep Learning models have achieved remarkable success. Training them is often accelerated by building on top of pre-trained models which poses the risk of perpetuating encoded biases. Here, we investigate biases in the representations of commonly used ImageNet classifiers for facial images while considering intersections of sensitive variables age, race and gender. To assess the biases, we use linear classifier probes and visualize activations as topographic maps. We find that representations in ImageNet classifiers particularly allow differentiation between ages. Less strongly pronounced, the models appear to associate certain ethnicities and distinguish genders in middle-aged groups. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_03664 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Assessing Intersectional Bias in Representations of Pre-Trained Image Recognition Models Krug, Valerie Stober, Sebastian Computer Vision and Pattern Recognition Computers and Society Human-Computer Interaction Machine Learning Deep Learning models have achieved remarkable success. Training them is often accelerated by building on top of pre-trained models which poses the risk of perpetuating encoded biases. Here, we investigate biases in the representations of commonly used ImageNet classifiers for facial images while considering intersections of sensitive variables age, race and gender. To assess the biases, we use linear classifier probes and visualize activations as topographic maps. We find that representations in ImageNet classifiers particularly allow differentiation between ages. Less strongly pronounced, the models appear to associate certain ethnicities and distinguish genders in middle-aged groups. |
| title | Assessing Intersectional Bias in Representations of Pre-Trained Image Recognition Models |
| topic | Computer Vision and Pattern Recognition Computers and Society Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2506.03664 |