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Explore the fundamental challenges and solutions in algorithmic fairness through this 56-minute conference talk that addresses two critical questions: what constitutes a fair algorithm and how to achieve fairness in practice. Examine why there is no universal definition of algorithmic fairness, as different contexts require different approaches that are often contradictory, making the selection of appropriate fairness definitions a complex policy decision. Discover how machine learning algorithms increasingly influence daily life, making fairness essential to prevent discrimination based on gender, ethnicity, and other protected characteristics. Learn about fairness in ad auction settings for job advertisements, where multiple fairness definitions may apply simultaneously, and understand how changing these definitions can provide advantages in achieving desired outcomes. Delve into the concept of omnipredictors with fairness constraints - predictors that can be efficiently post-processed to minimize various loss functions while maintaining fairness requirements. Understand how this approach allows for efficient handling of changing fairness constraints over time, which is particularly valuable since fairness requirements often evolve as policy decisions change. Gain insights into how algorithmic fairness principles extend beyond their traditional applications into complexity theory, and explore connections to quantum error correction research. The presentation concludes with a discussion of current research interests and future directions in the field of algorithmic fairness.
