  Fairness and robustness play vital roles in trustworthy machine learning.
Observing safety-critical needs in various annotation-expensive vision
applications, we introduce a novel learning framework, Fair Robust Active
Learning (FRAL), generalizing conventional active learning to fair and
adversarial robust scenarios. This framework allows us to achieve standard and
robust minimax fairness with limited acquired labels. In FRAL, we then observe
existing fairness-aware data selection strategies suffer from either
ineffectiveness under severe data imbalance or inefficiency due to huge
computations of adversarial training. To address these two problems, we develop
a novel Joint INconsistency (JIN) method exploiting prediction inconsistencies
between benign and adversarial inputs as well as between standard and robust
models. These two inconsistencies can be used to identify potential fairness
gains and data imbalance mitigations. Thus, by performing label acquisition
with our inconsistency-based ranking metrics, we can alleviate the class
imbalance issue and enhance minimax fairness with limited computation.
Extensive experiments on diverse datasets and sensitive groups demonstrate that
our method obtains the best results in standard and robust fairness under
white-box PGD attacks compared with existing active data selection baselines.
