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Found 8 papers, 2 papers with code
Evaluating ChatGPT as a Recommender System: A Rigorous Approach
Through thoroughly exploring ChatGPT's abilities in recommender systems, our study aims to contribute to the growing body of research on the versatility and potential applications of large language models.
Counterfactual Reasoning for Bias Evaluation and Detection in a Fairness under Unawareness setting
Our experiments show that, even if the model is trained without sensitive features, it often suffers discriminatory biases.
Counterfactual Fair Opportunity: Measuring Decision Model Fairness with Counterfactual Reasoning
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 attention of the research community.
MAFUS: a Framework to predict mortality risk in MAFLD subjects
Few articles are focused on mortality in MAFLD subjects, and none investigate how to predict a fatal outcome.
Explainable artificial intelligence
Explainable Artificial Intelligence (XAI)
Interactive Question Answering Systems: Literature Review
Interactive question answering is a recently proposed and increasingly popular solution that resides at the intersection of question answering and dialogue systems.
Conversational Recommendation: Theoretical Model and Complexity Analysis
With this work, we complement empirical research with a theoretical, domain-independent model of conversational recommendation.
FedeRank: User Controlled Feedback with Federated Recommender Systems
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life.
How to Put Users in Control of their Data in Federated Top-N Recommendation with Learning to Rank
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 vast space of possible choices.
