3 citations · 4 across the 6 of their papers we have counts for
6 papers
ReasonX: Declarative Reasoning on Explanations
Laura State, Salvatore Ruggieri, Franco Turini
Explaining opaque Machine Learning (ML) models has become an increasingly important challenge. However, current eXplanation in AI (XAI) methods suffer several shortcomings, includi…
The explanation dialogues: an expert focus study to understand requirements towards explanations within the GDPR
Laura State, Alejandra Bringas Colmenarejo, Andrea Beretta +3
Explainable AI (XAI) provides methods to understand non-interpretable machine learning models. However, we have little knowledge about what legal experts expect from these explanat…
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…
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…
Beyond Demographic Parity: Redefining Equal Treatment
Carlos Mougan, Laura State, Antonio Ferrara +2
Liberalism-oriented political philosophy reasons that all individuals should be treated equally independently of their protected characteristics. Related work in machine learning h…
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal
Laura State, Hadrien Salat, Stefania Rubrichi +1
Explainable artificial intelligence (XAI) provides explanations for not interpretable machine learning (ML) models. While many technical approaches exist, there is a lack of valida…