  Machine learning models that automate decision-making are increasingly being
used in consequential areas such as loan approvals, pretrial bail approval,
hiring, and many more. 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 may further require informing the
individual of changes in the input attribute that could be made to produce a
desirable outcome. This paper focuses on the latter problem of automatically
generating counterfactual explanations. We propose a framework Counterfactual
Generation with s(CASP) (CFGS) that utilizes answer set programming (ASP) and
the s(CASP) goal-directed ASP system to automatically generate counterfactual
explanations from rules generated by rule-based machine learning (RBML)
algorithms. In our framework, 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 we can navigate
between these worlds, namely, go from our original world/scenario where we
obtain an undesired outcome to the imagined world/scenario where we obtain a
desired/favourable outcome.
