Authors: Pooja S. B. Rao, Sanja Šćepanović, Ke Zhou, Edyta Paulina Bogucka, D Quercia
Year: 2025
Published in: ArXiv
Institution: Nokia Bell Labs, University of Lausanne
Research Area: AI Risk Management, Model Risk Reporting, RAG Pipeline, RAG
Discipline: Artificial Intelligence
RiskRAG improves AI model risk reporting by offering pre-populated, contextualized risk reports that are preferred by developers, designers, and media professionals over standard model cards.
Methods: Developed a Retrieval Augmented Generation system based on five design requirements co-created with 16 developers, using a dataset of 450K model cards and 600 real-world incidents. Evaluated RiskRAG in preliminary and final studies with a total of 125 participants.
Key Findings: Effectiveness of RiskRAG in improving risk reporting and decision-making compared to standard model cards.
Sample Size: 125