Discover the Unknown Biased Attribute of an Image Classifier

Zhiheng Li, Chenliang Xu

University of Rochester

Preprint: https://arxiv.org/abs/2104.14556

Contact: Zhiheng Li (email: zhiheng.li@rochester.edu, homepage: https://zhiheng.li)

Recent works find that AI algorithms learn biases from data. Therefore, it is urgent and vital to identify biases in AI algorithms. However, the previous bias identification pipeline overly relies on human experts to conjecture potential biases (e.g., gender), which may neglect other underlying biases not realized by humans. To help human experts better find the AI algorithms' biases, we study a new problem in this work -- for a classifier that predicts a target attribute of the input image, discover its unknown biased attribute.

To solve this challenging problem, we use a hyperplane in the generative model's latent space to represent an image attribute; thus, the original problem is transformed to optimizing the hyperplane's normal vector and offset. We propose a novel total-variation loss within this framework as the objective function and a new orthogonalization penalty as a constraint. The latter prevents trivial solutions in which the discovered biased attribute is identical with the target or one of the known-biased attributes. Extensive experiments on both disentanglement datasets and real-world datasets show that our method can discover biased attributes and achieve better disentanglement w.r.t. target attributes. Furthermore, the qualitative results show that our method can discover unnoticeable biased attributes for various object and scene classifiers, proving our method's generalizability for detecting biased attributes in diverse domains of images.

Here we show some interesting unknown biased attributes discovered by our method.

The classifier (first column)'s target prediction's probability (second column) gradually decreases as the image transforms (last two columns) under the discovered unknown biased attribute (third column).

For example, the ResNet-18 classifier trained on ImageNet's predicted probability of the "Cat" class gradually decreases as the cat's shade of fur color goes darker.

Classifier	Classfier's Target Prediction	Discovered Unknown Biased Attribute
ResNet-18 Trained on ImageNet [1]	Cat	shade of fur color (light $\rightarrow$ dark)
ResNet-18 Trained on Places365 [2]	Bedroom	number of beds (1 $\rightarrow$ 2)
ResNet-18 Trained on Places365 [2]	Bridge	buildings in the background (no building $\rightarrow$ building)
ResNet-18 Trained on Places365 [2]	Conference Room	layout of conference room (table $\rightarrow$ hollow square table / no table)
ResNet-18 Trained on Places365 [2]	Tower	is Eiffel Tower (Eiffel tower $\rightarrow$ other towers)

disentanglement_lib

pip install disentanglement-lib

PyTorch

torchvision

dSprites:

wget -O data/dsprites/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz https://github.com/deepmind/dsprites-dataset/blob/master/dsprites_ndarray_co1sh3sc6or40x32y32_64x64.npz?raw=true

SmallNorb:

1. Download the gz files from https://cs.nyu.edu/~ylclab/data/norb-v1.0-small/

2. decompress the files into data/small_norb

Step 1: train a biased target attribute classifier

bash scripts/synthetic/train_classifier.sh

Step 2: train a generative model, e.g., $\beta$-VAE.

bash scripts/synthetic/train_generative_model.sh

Step 3: encode images to the latent space via the encoder in the VAE-based model

bash scripts/synthetic/gen_latent_code.sh

Step 4: generate the ground-truth hyperplanes

bash scripts/synthetic/gen_gt_hyperplanes.sh

Step 5: optimize TV loss and orthogonalization penalty to get biased attribute hyperplane, i.e., discover the unknown biased attribute

bash scripts/synthetic/discover_unknown_bias.sh

Step 6: visualize the traversal images to interpret the bias

bash scripts/synthetic/visualize.sh

[1] J. Deng, W. Dong, R. Socher, L.-J. Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jun. 2009, pp. 248–255.

[2] B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 40, no. 6, pp. 1452–1464, 2018.

The code of "experiment on disentanglement datasets" is based on Disentanglement-PyTorch.

@inproceedings{li-2021-discover,
  title = {Discover the {{Unknown Biased Attribute}} of an {{Image Classifier}}},
  booktitle = {The {{IEEE International Conference}} on {{Computer Vision}} ({{ICCV}})},
  author = {Li, Zhiheng and Xu, Chenliang},
  year = {2021},
}