51 citations · 171 across the 26 of their papers we have counts for
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Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning
Giandomenico Cornacchia, Vito Walter Anelli, Fedelucio Narducci +2
The increasing application of Artificial Intelligence and Machine Learning models poses potential risks of unfair behavior and, in light of recent regulations, has attracted the at…
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting
Giandomenico Cornacchia, Vito Walter Anelli, Fedelucio Narducci +2
Current AI regulations require discarding sensitive features (e.g., gender, race, religion) in the algorithm's decision-making process to prevent unfair outcomes. However, even wit…
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +2
Recommendation services are extensively adopted in several user-centered applications as a tool to alleviate the information overload problem and help users in orienteering in a va…
Prioritized Multi-Criteria Federated Learning
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +1
In Machine Learning scenarios, privacy is a crucial concern when models have to be trained with private data coming from users of a service, such as a recommender system, a locatio…
Towards Effective Device-Aware Federated Learning
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +1
With the wealth of information produced by social networks, smartphones, medical or financial applications, speculations have been raised about the sensitivity of such data in term…