Epistemic vs Counterfactual Fairness in Allocation of Resources

Explore the fundamental differences between epistemic and counterfactual fairness concepts in resource allocation through this 14-minute conference talk from the Association for Computing Machinery. Examine how these two distinct fairness paradigms impact decision-making processes when distributing resources, analyzing the theoretical foundations and practical implications of each approach. Delve into the research presented by Hadi Hosseini, Joshua Kavner, Sujoy Sikdar, Rohit Vaish, and Lirong Xia as they compare epistemic fairness—which focuses on what is known or can be reasonably inferred—with counterfactual fairness—which considers hypothetical scenarios and alternative outcomes. Understand the computational and ethical challenges that arise when implementing these fairness criteria in real-world allocation systems, and discover how different fairness definitions can lead to varying resource distribution outcomes. Gain insights into the ongoing debates within the algorithmic fairness community regarding which fairness notion is most appropriate for different allocation contexts, and learn about the trade-offs between these approaches in terms of both theoretical rigor and practical applicability.