The pursuit of trustworthy Artificial Intelligence hinges on rigorous and innovative testing practices. This presentation surveys the key elements involved in achieving reliable AI systems. It will cover the impact of current and forthcoming AI regulations on testing requirements, with attention to the evolving European framework. We will then review practical testing techniques, showcasing the utility of “LLM-as-a-Judge” for sophisticated output evaluation and “Agent-Based Simulation” for understanding AI in complex interactive environments. The presentation aims to map out the terrain of existing work and, more importantly, to illuminate compelling research opportunities for PhD students looking to contribute to this essential and rapidly advancing field.
Testing Trustworthy AI: Bridging Regulation, Research Frontiers, and Practical Implementation Challenges
14 July 2025
2:15 pm - 5:00 pm
Please note that this event is part of the Safe & Trusted AI Summer School.
About the speaker
Dr. Edgar Lopez-Rojas is the CEO and Founder of RevAIsor, a London-based company that aims to empower positive change with trustworthy and ethical AI. He holds a PhD in Computer Science from BTH in Sweden. He has over 20 years of experience in software development, data analytics, and computational methods for Security and Financial Crime Analytics.
Dr. Lopez-Rojas is also the visionary Founder and Chief Visionary Officer of FinCrime Dynamics, a company specialising in Simulation for Financial Crime Analytics using Synthetic Data. He invented the “FinCrime Vaccines” concept, a groundbreaking approach to enhancing Anti-Money Laundering Compliance and Optimisation within the financial industry. He is an Innovate UK awardee, a prominent international speaker, and a distinguished researcher who pioneered methods to synthesise financial transactions and measure the performance and effectiveness of FinCrime analytics. He is passionate about harnessing the potential of cutting-edge technologies and innovative approaches to address the risks and challenges posed by AI biases, ethical concerns, and regulatory complexities.