DiCE: Counterfactual Explanations offer clarity in AI decision-making

Consider a person who applies for a loan with a financial company, but their application is rejected by a machine learning algorithm used to determine who receives a loan from the company. How would you explain the decision made by the algorithm to this person? One option is to provide them with a list of features that contributed to the algorithm's decision, such as income and credit score. Many of the current explanation methods provide this information by either analyzing the algorithm's properties or approximating it with a simpler, interpretable model. However, these explanations do not help this person decide what to do next to increase their chances of getting the loan in the future.