  Counterfactual explanation methods interpret the outputs of a machine
learning model in the form of "what-if scenarios" without compromising the
fidelity-interpretability trade-off. They explain how to obtain a desired
prediction from the model by recommending small changes to the input features,
aka recourse. We believe an actionable recourse should be created based on
sound counterfactual explanations originating from the distribution of the
ground-truth data and linked to the domain knowledge. Moreover, it needs to
preserve the coherency between changed/unchanged features while satisfying
user/domain-specified constraints. This paper introduces CARE, a modular
explanation framework that addresses the model- and user-level desiderata in a
consecutive and structured manner. We tackle the existing requirements by
proposing novel and efficient solutions that are formulated in a
multi-objective optimization framework. The designed framework enables
including arbitrary requirements and generating counterfactual explanations and
actionable recourse by choice. As a model-agnostic approach, CARE generates
multiple, diverse explanations for any black-box model in tabular
classification and regression settings. Several experiments on standard data
sets and black-box models demonstrate the effectiveness of our modular
framework and its superior performance compared to the baselines.

Machine Learning;Artificial Intelligence