Seller Side Outcome Fairness

Seller-side outcome fairness in online marketplaces addresses the inequitable exposure of sellers and their products to buyers, aiming to ensure fair revenue distribution and marketplace diversity. Current research focuses on developing algorithms and optimization models that balance maximizing overall platform metrics (like revenue) with fairness metrics, often employing techniques from causal inference, bandit theory, and reinforcement learning to achieve this balance. This research is significant because it tackles the ethical and economic implications of algorithmic bias in online platforms, potentially leading to more equitable and sustainable e-commerce ecosystems.