5 papers
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Trust in Vision-Language Models: Insights from a Participatory User Workshop
Agnese Chiatti, Lara Piccolo, Sara Bernardini +2
With the growing deployment of Vision-Language Models (VLMs), pre-trained on large image-text and video-text datasets, it is critical to equip users with the tools to discern when…
Mapping User Trust in Vision Language Models: Research Landscape, Challenges, and Prospects
Agnese Chiatti, Sara Bernardini, Lara Shibelski Godoy Piccolo +2
The rapid adoption of Vision Language Models (VLMs), pre-trained on large image-text and video-text datasets, calls for protecting and informing users about when to trust these sys…
Mutual Understanding between People and Systems via Neurosymbolic AI and Knowledge Graphs
Irene Celino, Mario Scrocca, Agnese Chiatti
This chapter investigates the concept of mutual understanding between humans and systems, positing that Neuro-symbolic Artificial Intelligence (NeSy AI) methods can significantly e…
Neuro-Symbolic Scene Graph Conditioning for Synthetic Image Dataset Generation
Giacomo Savazzi, Eugenio Lomurno, Cristian Sbrolli +2
As machine learning models increase in scale and complexity, obtaining sufficient training data has become a critical bottleneck due to acquisition costs, privacy constraints, and…