Join us for an expert discussion on the tools and methods available to build and deploy trustworthy AI models.
Artificial intelligence holds tremendous potential to create value for organizations and broader society. A McKinsey study estimates that AI applications could generate $3.5 to $5.8 trillion in value per year.
Yet, a tiny percentage of these companies has managed to deploy AI at scale and the issue of AI’s trustworthiness – or lack thereof – has often been seen as a key barrier.
Indeed, it is now established that without proper oversight, AI may generate errors, exacerbate human bias, lead to opaque decisions or cause potential privacy issues.
Ensuring that AI models are trustworthy is particularly critical for startup companies because opaque, poorly performing or unfair models will alienate clients, draw regulatory scrutiny and turn investors off.How do you address these challenges?
Panelists: 🎤 Lofred Madzou, Director of Strategy and Business Development at Truera, and research associate at the Oxford Internet Institute focusing on AI audit. Previously, he was an AI Lead at the World Economic Forum. He also co-drafted the French AI National Strategy. 🎤 Philipp Slusallek, Scientific Director at the German Research Center for Artificial Intelligence (DFKI), where he has led the research area “Agents and Simulated Reality” since 2008. At Saarland University, he has been a full professor for Computer Graphics since 1999. 🎤 Sebastian Hallensleben is Chair of CEN-CENELEC JTC 21 where European AI standards to underpin EU regulation are being developed, a member of the Expert Advisory Board of the EU StandICT programme and Chair of the Trusted Information working group. He heads Digitalisation and Artificial Intelligence at VDE Association for Electrical, Electronic and Information Technologies.
The expert panel will cover:
What are the biggest challenges with getting AI models to production
What are the requirements of a Trustworthy AI model
What tools and methods can you use to test your AI model’s trustworthiness
How to debug an AI model to increase its trustworthiness
How to monitor ML models at scale and maintain their trustworthiness
The panel discussion will be followed by a Q&A – where all participants can pose questions and share their perspectives – then cocktails and dinner.