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Found 10 papers, 5 papers with code
The Social Impact of Generative AI: An Analysis on ChatGPT
In recent months, the social impact of Artificial Intelligence (AI) has gained considerable public interest, driven by the emergence of Generative AI models, ChatGPT in particular.
A Rapid Review of Responsible AI frameworks: How to guide the development of ethical AI
In the last years, the raise of Artificial Intelligence (AI), and its pervasiveness in our lives, has sparked a flourishing debate about the ethical principles that should lead its implementation and use in society.
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.
How to make latent factors interpretable by feeding Factorization machines with knowledge graphs
By relying on the information encoded in the original knowledge graph, we have also evaluated the semantic accuracy and robustness for the knowledge-aware interpretability of the final model.
On the discriminative power of Hyper-parameters in Cross-Validation and how to choose them
Hyper-parameters tuning is a crucial task to make a model perform at its best.
Knowledge-aware Autoencoders for Explainable Recommender Sytems
Recommender Systems have been widely used to help users in finding what they are looking for thus tackling the information overload problem.
Computing recommendations via a Knowledge Graph-aware Autoencoder
In the last years, deep learning has shown to be a game-changing technology in artificial intelligence thanks to the numerous successes it reached in diverse application fields.
Local Popularity and Time in top-N Recommendation
Items popularity is a strong signal in recommendation algorithms.
The importance of being dissimilar in Recommendation
Similarity measures play a fundamental role in memory-based nearest neighbors approaches.
