Overview

Addressing AI model risks requires systematic risk communication and reporting across the AI lifecycle. This research investigates developer-reported risks in documentation compared to real-world AI incidents, and introduces data-driven tools for contextual risk reporting.

Related Publications

1. Pooja S. B. Rao, Sanja Šćepanović, Dinesh Babu Jayagopi, Mauro Cherubini, and Daniele Quercia, “The AI Model Risk Catalog: What Developers and Researchers Miss About Real-World AI Harms.” AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2025.
2. Pooja S. B. Rao, Sanja Šćepanović, Ke Zhou, Edyta Paulina Bogucka, Daniele Quercia, “RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting.” ACM CHI Conference on Human Factors in Computing Systems (CHI ’25) 2025.
3. Pooja S. B. Rao, Laetitia Renier, Marc-Olivier Boldi, Marianne S. Mast, Dinesh B. Jayagopi and Mauro Cherubini, “On the Potential of Supporting Autonomy in Online Video Interview Training Platforms.” International Journal of Human-Computer Studies (IJHCS) 2024.