  The interpretability of complex Machine Learning models is coming to be a
critical social concern, as they are increasingly used in human-related
decision-making processes such as resume filtering or loan applications.
Individuals receiving an undesired classification are likely to call for an
explanation -- preferably one that specifies what they should do in order to
alter that decision when they reapply in the future. Existing work focuses on a
single ML model and a single point in time, whereas in practice, both models
and data evolve over time: an explanation for an application rejection in 2018
may be irrelevant in 2019 since in the meantime both the model and the
applicant's data can change. To this end, we propose a novel framework that
provides users with insights and plans for changing their classification in
particular future time points. The solution is based on combining
state-of-the-art algorithms for (single) model explanations, ones for
predicting future models, and database-style querying of the obtained
explanations. We propose to demonstrate the usefulness of our solution in the
context of loan applications, and interactively engage the audience in
computing and viewing suggestions tailored for applicants based on their unique
characteristic.
