Counterfactual Analysis is a powerful tool in Explainable Machine Learning. Given a classifier and a record, one seeks the smallest perturbation necessary to have the perturbed record, called the counterfactual explanation, classified in the desired class. Feature uncertainty in data reflects the inherent variability and noise present in real-world scenarios, and therefore, there is a … Read more
Counterfactual explanations (CEs) offer a human-understandable way to explain decisions by identifying specific changes to the input parameters of a base or present model that would lead to a desired change in its outcome. For optimization models, CEs have primarily been studied in limited contexts, such as linear optimization problems with continuous decision variables or … Read more
Explainable artificial intelligence is one of the most important trends in modern machine-learning research. The idea is to explain the outcome of a model by presenting a certain change in the input of the model so that the outcome changes significantly. In this paper, we study this question for linear optimization problems as an automated … Read more