Jacques S
- Research Program Mentor
PhD candidate at University of Southern California (USC)
Expertise
Human-Ai interaction, AI ethics, AI regulation, Assessing AI bias
Bio
I am a Neuroscience PhD student at the University of Southern California, where I study human AI interaction. I focus on how people change their behavior, decision-making, and sense of responsibility when working with AI systems. I examine these questions both at the behavioral level and at the level of the brain, using fMRI to study how brain activity changes during collaboration with artificial intelligence. My research explores how AI influences trust, moral judgment, and the way we think and make decisions. Before starting my PhD, I studied computer science with a focus on brain-inspired artificial intelligence and machine learning. This background shapes how I approach research. I am interested in how neuroscience can help improve AI design, and how AI systems, in turn, affect human thinking and behavior. My work brings together neuroscience, computer science, and philosophy. I enjoy mentoring students who want to build technical skills while also exploring big questions about intelligence, responsibility, and the future of human AI collaboration. Outside of the lab, I care deeply about community and public life. I spend time working on local projects in Los Angeles around housing, labor, and culture, and I love finding ways to connect people through events and storytelling. I also have a soft spot for plants, film, and coffee.Project ideas
Evaluating the Moral Reasoning of Contemporary AI Systems
Produce a rigorous, reproducible literature review that summarizes how current LLMs and related AI systems perform on moral and ethical tasks, synthesizes evaluation methods and datasets, identifies major gaps, and recommends next research directions and benchmark practices suitable for publication.
Detecting AI hallucinations
Build a simple system that flags potentially hallucinated AI outputs using: (1) Retrieval checks (2) Fact consistency scoring (3) Multiple-model agreement
Fake News Classification Model
This project involves building a simple machine learning model that can classify news articles as real or fake based on their text. The student would use a publicly available dataset, learn how to clean and prepare text data, and train a basic model such as logistic regression to make predictions. They would evaluate how well the model performs using clear metrics like accuracy and analyze examples where the model makes mistakes, learning about both the power and limits of AI systems. The final outcome is a working fake news classifier and a short report explaining the model’s performance and insights.
Detecting Bias in Language Models
Test whether an open-source LLM shows bias in hiring, education, or criminal justice prompts.