  Counterfactual explanations are a prominent example of post-hoc
interpretability methods in the explainable Artificial Intelligence research
domain. They provide individuals with alternative scenarios and a set of
recommendations to achieve a sought-after machine learning model outcome.
Recently, the literature has identified desiderata of counterfactual
explanations, such as feasibility, actionability and sparsity that should
support their applicability in real-world contexts. However, we show that the
literature has neglected the problem of the time dependency of counterfactual
explanations. We argue that, due to their time dependency and because of the
provision of recommendations, even feasible, actionable and sparse
counterfactual explanations may not be appropriate in real-world applications.
This is due to the possible emergence of what we call "unfortunate
counterfactual events." These events may occur due to the retraining of machine
learning models whose outcomes have to be explained via counterfactual
explanation. Series of unfortunate counterfactual events frustrate the efforts
of those individuals who successfully implemented the recommendations of
counterfactual explanations. This negatively affects people's trust in the
ability of institutions to provide machine learning-supported decisions
consistently. We introduce an approach to address the problem of the emergence
of unfortunate counterfactual events that makes use of histories of
counterfactual explanations. In the final part of the paper we propose an
ethical analysis of two distinct strategies to cope with the challenge of
unfortunate counterfactual events. We show that they respond to an ethically
responsible imperative to preserve the trustworthiness of credit lending
organizations, the decision models they employ, and the social-economic
function of credit lending.
