On the Human Element in Building Trustworthy Consumer-Centric AI Products in Banking
As the demand for personalized AI-empowered assistants along the value chain in banking is growing, the reliability and interpretability of their underlying models become paramount to build trust and sustainable value for consumers. This presentation focuses on human-augmented training of domain-specific neural networks and discusses their use cases and recent advances in methods to address model transparency, adversarial robustness, algorithmic bias and fairness in the context of the current state and perspectives on the evolution of the global regulatory landscape around AI.
Manuel is currently Lead Data Scientist and Head of Predictive Analytics in Banking Products at UBS. Previously, he's been a senior advisor and AI cloud platform lead at Ernst & Young, developed numerous AI-driven business solutions for international organizations, and held managing roles in cross-border audit & advisory engagements and leading international research collaborations with contributions to AI research, Cognitive Systems and Particle Physics.