  Prioritizing fairness is of central importance in artificial intelligence
(AI) systems, especially for those societal applications, e.g., hiring systems
should recommend applicants equally from different demographic groups, and risk
assessment systems must eliminate racism in criminal justice. Existing efforts
towards the ethical development of AI systems have leveraged data science to
mitigate biases in the training set or introduced fairness principles into the
training process. For a deployed AI system, however, it may not allow for
retraining or tuning in practice. By contrast, we propose a more flexible
approach, i.e., fairness-aware adversarial perturbation (FAAP), which learns to
perturb input data to blind deployed models on fairness-related features, e.g.,
gender and ethnicity. The key advantage is that FAAP does not modify deployed
models in terms of parameters and structures. To achieve this, we design a
discriminator to distinguish fairness-related attributes based on latent
representations from deployed models. Meanwhile, a perturbation generator is
trained against the discriminator, such that no fairness-related features could
be extracted from perturbed inputs. Exhaustive experimental evaluation
demonstrates the effectiveness and superior performance of the proposed FAAP.
In addition, FAAP is validated on real-world commercial deployments
(inaccessible to model parameters), which shows the transferability of FAAP,
foreseeing the potential of black-box adaptation.
