  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. The code is available at
https://git.io/J3kMh.
