Jasone Ramirez-Ayerbe

Universidad de Sevilla | US · Statistics and Operations Research

Counterfactual Analysis has shown to be a powerful tool in the burgeoning field of Explainable Artificial Intelligence. In Supervised Classification, this means associating with each record a so-called counterfactual explanation: an instance that is close to the record and whose probability of being classified in the positive class by a given class...

Traditional benchmarking based on simple key performance indicators is widely used and easy to understand. Unfortunately, such indicators cannot fully capture the complex relationship between multiple inputs and outputs in most firms. Data Envelopment Analysis (DEA) offers an attractive alternative. It builds an activity analysis model of best prac...

Counterfactual explanations have become a very popular interpretability tool to understand and explain how complex machine learning models make decisions for individual instances. Most of the research on counterfactual explainability focuses on tabular and image data and much less on models dealing with functional data. In this paper, a counterfact...

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to be able to understand how models arrive at decisions. Assuming an already trained Supervised Classification model, an effective class of post-hoc explanations are counterfactual explanations, i.e., a set of actions t...