  Machine learning models that automate decision-making are increasingly used
in consequential areas such as loan approvals, pretrial bail approval, and
hiring. Unfortunately, most of these models are black boxes, i.e., they are
unable to reveal how they reach these prediction decisions. A need for
transparency demands justification for such predictions. An affected individual
might also desire explanations to understand why a decision was made. Ethical
and legal considerations require informing the individual of changes in the
input attribute (s) that could be made to produce a desirable outcome. Our work
focuses on the latter problem of generating counterfactual explanations by
considering the causal dependencies between features. In this paper, we present
the framework CFGs, CounterFactual Generation with s(CASP), which utilizes the
goal-directed Answer Set Programming (ASP) system s(CASP) to automatically
generate counterfactual explanations from models generated by rule-based
machine learning algorithms in particular. We benchmark CFGs with the FOLD-SE
model. Reaching the counterfactual state from the initial state is planned and
achieved using a series of interventions. To validate our proposal, we show how
counterfactual explanations are computed and justified by imagining worlds
where some or all factual assumptions are altered/changed. More importantly, we
show how CFGs navigates between these worlds, namely, go from our initial state
where we obtain an undesired outcome to the imagined goal state where we obtain
the desired decision, taking into account the causal relationships among
features.
