Fairness in Machine Learning - From Theory to Practice

Explore the complex issue of fairness in machine learning through this 42-minute sponsored session presented by Alex Karsten from GitLab. Delve into the integration of machine learning in daily life and its potential unintended consequences. Examine various perspectives on ensuring fairness and justice in machine learning models while acknowledging existing challenges and limitations. Investigate the meaning of "fairness" in the context of machine learning, methods for measuring fairness, and appropriate metrics. Identify potential sources of bias in machine learning and strategies to address them. Consider the role of regulation and policy in promoting fairness within the field. Gain insights into the multifaceted nature of fairness in machine learning and its practical implications.

Sponsored Session: Fairness in Machine Learning: From Theory to Practice - Alex Karsten, GitLab