  Assured AI in unrestricted settings is a critical problem. Our framework
addresses AI assurance challenges lying at the intersection of domain
adaptation, fairness, and counterfactuals analysis, operating via the discovery
and intervention on factors of variations in data (e.g. weather or illumination
conditions) that significantly affect the robustness of AI models. Robustness
is understood here as insensitivity of the model performance to variations in
sensitive factors. Sensitive factors are traditionally set in a supervised
setting, whereby factors are known a-priori (e.g. for fairness this could be
factors like sex or race). In contrast, our motivation is real-life scenarios
where less, or nothing, is actually known a-priori about certain factors that
cause models to fail. This leads us to consider various settings (unsupervised,
domain generalization, semi-supervised) that correspond to different degrees of
incomplete knowledge about those factors. Therefore, our two step approach
works by a) discovering sensitive factors that cause AI systems to fail in a
unsupervised fashion, and then b) intervening models to lessen these factor's
influence. Our method considers 3 interventions consisting of Augmentation,
Coherence, and Adversarial Interventions (ACAI). We demonstrate the ability for
interventions on discovered/source factors to generalize to target/real
factors. We also demonstrate how adaptation to real factors of variations can
be performed in the semi-supervised case where some target factor labels are
known, via automated intervention selection. Experiments show that our approach
improves on baseline models, with regard to achieving optimal utility vs.
sensitivity/robustness tradeoffs.
