11 citations · 11 across the 4 of their papers we have counts for
4 papers
Causal Fairness-Guided Dataset Reweighting using Neural Networks
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri +1
The importance of achieving fairness in machine learning models cannot be overstated. Recent research has pointed out that fairness should be examined from a causal perspective, an…
Declarative Reasoning on Explanations Using Constraint Logic Programming
Laura State, Salvatore Ruggieri, Franco Turini
Explaining opaque Machine Learning (ML) models is an increasingly relevant problem. Current explanation in AI (XAI) methods suffer several shortcomings, among others an insufficien…
Ensemble of Counterfactual Explainers
Riccardo Guidotti, Salvatore Ruggieri
In eXplainable Artificial Intelligence (XAI), several counterfactual explainers have been proposed, each focusing on some desirable properties of counterfactual instances: minimali…
Reason to explain: Interactive contrastive explanations (REASONX)
Laura State, Salvatore Ruggieri, Franco Turini
Many high-performing machine learning models are not interpretable. As they are increasingly used in decision scenarios that can critically affect individuals, it is necessary to d…