Algorithmic Fairness in the Real World: Bridging Theory and Practice

This comprehensive study examines how algorithmic fairness principles can be effectively implemented in real-world applications. The authors analyze the gap between theoretical fairness metrics and practical challenges in deployment.

The research provides concrete examples of how bias can manifest in machine learning systems and offers practical strategies for detecting and mitigating unfairness in automated decision-making systems.

The paper emphasizes the importance of considering social context and stakeholder engagement in developing fair algorithms.