The goal of the project is to investigate the role of social norms on misinformation in online communities. This knowledge can help identify new interventions in online communities that help prevent the spread of misinformation. To accomplish the task, the role of norms will be explored by analyzing Twitter data gathered through the Covid19 Infodemics Observatory, an online platform developed to study the relationship between the evolution of the COVID-19 epidemic and the information dynamics on social media. This study can inform a further set of microprojects addressing norms in AI systems through theoretical modelling and social simulations.
Output
Diagnosis and visualization map of existing social norms underlying fake news related to COVID19
Presentations
Project Partners:
Consiglio Nazionale delle Ricerche (CNR), ISTC: Eugenia Polizzi)
Fondazione Bruno Kessler (FBK), Marco Pistore
Primary Contact: Eugenia Polizzi, CNR-ISTC
WP4 Societal AI
socially aware interactions
Umeå University (UMU), Instituto Superior Técnico (IST)
In order for systems to function effectively in cooperations with humans and other AI systems they have to be aware of their social context. Especially in their interactions they should take into account the social aspects of their context, but also can use their social context to manage the interactions. Using the social context in the deliberation about the interaction steps will allow for an effective and focused dialogue that is geared towards a specific goal that is accepted by all parties in the interactions.
In this project we will start with the Dialogue Trainer system that allows for authoring very simple but directed dialogues to train (medical) students to have effective conversations with patients. Based on this tool, in which social context is taken into account only through the authors of the dialogue, we will design a system that will actually deliberate about the social context.
Output
software prototype for a flexible dialogue trainer system
CONVERSATIONS workshop paper 2021
Presentations
Project Partners:
Umeå University (UMU), Frank Dignum
Instituto Superior Técnico (IST), Rui Prada
Primary Contact: Frank Dignum, Umeå University
Main results of micro project:
The "Socially Aware Interactions" micro-project aims to address the following limitations of scripted dialogue training systems:
– Dialogue is not self-made: players are unable to learn relevant communication skills
– Dialogue is predetermined: agent does not need to adapt to changes in the context
– Dialogue tree is very large: editor may have difficulty managing the dialogue
Therefore, this project's goal is the creation of a flexible dialogue system, in which a socially aware conversational agent will deliberate and provide context-appropriate responses to users, based on defined social practices, identities, values, or norms. Scenarios in this dialogue system should be easy to author as well.
The main result is a Python prototype of a dialogue system with an architecture based on Cognitive Social Frames and Social Practices, whose dialogue scenarios are easy to edit in a widely used tool called Twine. We also submitted a workshop paper.
Contribution to the objectives of HumaneAI-net WPs
First, the dialogue system's flexibility and context-awareness will make the conversational agent appear more natural/realistic to the user, which is significant for the "Human-AI collaboration and interaction" work package.
Furthermore, in the system, the agent and the human user, besides having their own individual goals, are also attempting to achieve a dialogue goal together (e.g., in an anamnesis scenario, the main goal could be to obtain/give a diagnosis), which satisfies the "Societal AI" work package's goal "AI systems' individual vs collective goals".
This last work package includes the goal "Multimodal perception of awareness, emotions, and attitudes" as well, which is met because the agent adapts to changes in context, deliberating on top of it, and becoming more socially aware.
