7 citations · 10 across the 9 of their papers we have counts for
9 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…
Enhancing Fairness through Reweighting: A Path to Attain the Sufficiency Rule
Xuan Zhao, Klaus Broelemann, Salvatore Ruggieri +1
We introduce an innovative approach to enhancing the empirical risk minimization (ERM) process in model training through a refined reweighting scheme of the training data to enhanc…
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…
The Initial Screening Order Problem
Jose M. Alvarez, Antonio Mastropietro, Salvatore Ruggieri
We investigate the role of the initial screening order (ISO) in candidate screening. The ISO refers to the order in which the screener searches the candidate pool when selecting $k…
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…
Domain Adaptive Decision Trees: Implications for Accuracy and Fairness
Jose M. Alvarez, Kristen M. Scott, Salvatore Ruggieri +1
In uses of pre-trained machine learning models, it is a known issue that the target population in which the model is being deployed may not have been reflected in the source popula…