Explore the boundaries of group fairness and predictive multiplicity in machine learning through this insightful lecture by Flavio Calmon from Harvard University. Delve into information-theoretic methods for developing trustworthy machine learning systems, examining the challenges and limitations of achieving fairness across different groups. Gain a deeper understanding of how multiple predictive models can impact decision-making processes and the implications for creating more equitable AI systems.