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Mauro Dragoni

2 accepted papers

2025

Neutral Is Not Unbiased: Evaluating Implicit and Intersectional Identity Bias in LLMs Through Structured Narrative Scenarios

EMNLP 2025

Large Language Models often reproduce societal biases, yet most evaluations overlook how such biases evolve across nuanced contexts or intersecting identities. We introduce a scenario-based evaluation framework built on 100 narrative tasks, designed to be neutral at baseline and systematically modif

Cited by 0SourcePDFScholar
2022

Machine Learning for Utility Prediction in Argument-Based Computational Persuasion

AAAI 2022technical

Automated persuasion systems (APS) aim to persuade a user to believe something by entering into a dialogue in which arguments and counterarguments are exchanged. To maximize the probability that an APS is successful in persuading a user, it can identify a global policy that will allow it to select t…