  Explainable machine learning (ML) has gained traction in recent years due to
the increasing adoption of ML-based systems in many sectors. Algorithmic
Recourses (ARs) provide "what if" feedback of the form "if an input datapoint
were x' instead of x, then an ML-based system's output would be y' instead of
y." ARs are attractive due to their actionable feedback, amenability to
existing legal frameworks, and fidelity to the underlying ML model. Yet,
current AR approaches are single shot -- that is, they assume x can change to
x' in a single time period. We propose a novel stochastic-control-based
approach that generates sequential ARs, that is, ARs that allow x to move
stochastically and sequentially across intermediate states to a final state x'.
Our approach is model agnostic and black box. Furthermore, the calculation of
ARs is amortized such that once trained, it applies to multiple datapoints
without the need for re-optimization. In addition to these primary
characteristics, our approach admits optional desiderata such as adherence to
the data manifold, respect for causal relations, and sparsity -- identified by
past research as desirable properties of ARs. We evaluate our approach using
three real-world datasets and show successful generation of sequential ARs that
respect other recourse desiderata.
