Title: Humans and AI: Persuasion and Trust

Abstract: Generative AI (GenAI) models enable creators to produce novel content at scale. However, these new tools may also allow to create content which has an intent to persuade and drive people towards certain behaviours.

In this talk, we will discuss the challenges and opportunities of GenAI when deployed to generate multimodal, persuasive, personalised content at scale, as well as the use of AI to detect persuasive and propagandistic content automatically. We will then discuss how these GenAI models can be adapted to represent certain political ideologies, and how people make use and trust AI agents completing tasks for them.

Title: Topics, Lexicons and Controversies in Daily Moral Dilemmas: What I Learned From /r/AITA

Abstract: Moral dilemmas play an important role in theorizing both about ethical norms and moral psychology. Yet thought experiments borrowed from the philosophical literature often lack the nuances and complexity of real life. We use 100,000 threads from Reddit's r/AmItheAsshole to examine the features of everyday moral dilemmas. We discover 47 finer-grained, meaningful topics and group them into five meta-categories. We show that most dilemmas combine at least two topics, such as family and money. We also observe that the pattern of topic co-occurrence carries interesting information about the structure of everyday moral concerns. The incompleteness and fragility of their lexicons and from poor generalization across data domains. We fine-tune a transformer language model to measure moral foundations in text based on diverse data domains. The resulting model, called Mformer, outperforms existing approaches on the same domains and further generalizes well to four commonly used moral text datasets, improving by up to 17% in AUC. Our ongoing work is on understanding the malleability of daily moral judgements under the lens of group controversy and decision uncertainty.