Dear all / Bonjour à tous,

We are happy to invite you to Dorsaf Sallami PhD defense on December 9th at 10 am.

Vous êtes cordialement invité.e.s à la soutenance de thèse de Dorsaf Sallami, le 9 decembre  à 10h.

La soutenance se déroulera en anglais. 

Title: Toward Socially Responsible Artificial Intelligence Approaches for Fake News Detection

Date: December 9th, at 10 am.

Room: Pavillon André-Aisenstadt, salle 3195

Jury

Abstract:

Once celebrated as a common good that transformed knowledge sharing, the Internet—and social media platforms in particular—has connected communities across borders and exponentially expanded the scale of information exchange. Yet, these infrastructures have also become fertile ground for disinformation. Driven by algorithms that prioritize virality over veracity, fake news often spreads faster than established facts.

Artificial Intelligence (AI) thus appears as a promising tool. Machine learning models enable detection at scales unreachable by human fact-checkers, filtering and identifying problematic content in real time. However, the reality is more complex. Unlike other applications of machine learning designed to optimize efficiency in low-stakes contexts, fake news detection lies at the heart of democratic debate, public trust, and epistemic integrity. This dual observation calls for greater equity, transparency, and robustness. A high-performing model alone is insufficient, especially since fake news can originate early in the information cycle and persist long after initial detection, necessitating coordinated interventions before, during, and after its dissemination.

On the theoretical level, I propose two complementary directions. First, I argue for reframing fake news detection within the framework of Socially Responsible AI (SRAI).
Rather than focusing solely on accuracy, I advocate for explicit alignment with broader societal values—equity, transparency and robustness. Second, I contend that fake news
cannot be reduced to a mere detection problem; instead, it unfolds across a chain of events that begins well before any intervention and extends beyond it. While few studies consider
the full spectrum of actions that can deter or prevent the creation and dissemination of false content, I address this gap by proposing an interdisciplinary taxonomy of interventions
designed to deter, prevent, and mitigate fake news.

On the practical level, my contributions unfold in four parts. First, I conduct, to the best of my knowledge, the first study on gender bias in fake news detection and introduce
a classifier-adversary integration scheme to reduce inter-group disparities while maintaining competitive performance. Second, to generalize across heterogeneous domains, I propose
CoALFake, a cross-domain approach that combines domain-aware active learning with human–Large Language Model (LLM) co-annotation. LLMs perform large-scale preliminary labelling, while humans in the loop arbitrate ambiguous cases and correct errors. CoALFake achieves significant improvements in cross-domain robustness while reducing annotation costs. Third, I enhance explainability and user trust through the M ulti-level, Model-Agnostic Post-hoc Explanations (MAPE) system, which provides a multi-layered structure allowing users to adjust the level of detail to their expertise and decision-making needs. Fourth, I present Aletheia, a browser extension combining Retrieval-Augmented Generation (RAG) and LLMs to detect fake news and deliver evidence-based explanations directly within the browsing environment. In addition to real-time detection, Aletheia integrates two interactive features. The first promotes dialogue and collaborative content evaluation through a discussion space, while the second highlights the most recent fact-checks.