Fairgate enables adaptive fairness choices for recommendation rankings
FAIRGate: Agentic Runtime Policy Selection and Verified Repair for Fairer Recommendation Delivery
Information Retrieval
Summary
Recommendation systems often try to be fair by following one rule, but fairness needs can change a lot depending on the situation. The authors propose FAIRGate, a system that can pick from different fairness rules on the fly based on the current context. It checks that the chosen approach works properly and keeps a clear record to make sure fairness is maintained and reviewable. This helps recommendation systems treat items and users more fairly in changing conditions.
What this means in practice
- •For recommendation system engineers: Implement dynamic fairness policy selection to adapt recommendation outputs to runtime conditions and maintain measurable fairness guarantees.
- •For online platform operators: Use verified fairness repair and auditing to ensure recommendation fairness objectives hold during live deployment under changing item pools and user behavior.
Authors
Navkar Jain, Enock Ayiku, Kenneth Fletcher
Abstract
Fairness-aware recommender systems often encode one fairness objective during training or apply a fixed re-ranking rule after scoring. In deployment, however, the appropriate fairness action can vary across requests because the candidate pool, item metadata, exposure debt, user preference history, and utility budget all change at runtime. We propose FAIRGate, a model-agnostic delivery controller that places an agentic policy-selection layer between a base ranker and the final top-K list. The agent does not define fairness objectives from scratch. It chooses from a predefined policy library covering long-tail exposure, item-group exposure balance, concentration control, calibration protection, and abstention. A deterministic executor then applies the selected policy through constrained repair, and a validator accepts the result only when relevance, feasibility, metadata, calibration, and audit constraints are satisfied. The final language-model component receives only the verified audit trace and produces evidence-linked narration after the ranking decision is fixed. This design gives the agent runtime responsibility while keeping fairness enforcement measurable, bounded, and replayable.