Fairness Dynamics in Digital Economy Platforms with Biased Ratings

2026-02-19 · Source: cs.MA updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, E-commerce & Digital Commerce · Depth: Expert, extended

Summary

This paper introduces an evolutionary game theoretical model to analyze fairness dynamics in digital economy platforms, specifically focusing on how biased rating systems affect service providers and consumers. The model, presented at the 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), investigates platform design decisions regarding promoting service providers based on reputation or protected group status. It reveals a fundamental trade-off between user experience (UX) and fairness: promoting highly-rated providers benefits users but reduces demand for marginalized providers affected by rating bias. The research demonstrates that actively intervening by tuning the demographics of search results, even with uncertainty about the bias level, is highly effective in reducing unfairness with minimal user impact. The model highlights the benefits of proactive anti-discrimination design in systems that use ratings to foster cooperative behavior.

Key takeaway

For product managers and platform designers grappling with fairness in digital economies, this research indicates that explicitly prioritizing marginalized providers in recommender systems (via kM) can significantly improve demographic parity without substantially degrading user experience. You should actively implement anti-discrimination policies, even under uncertainty about the precise level of rating bias, as inaction or passive approaches lead to less equitable outcomes. This proactive stance is crucial for supporting vulnerable populations reliant on platform labor.

Key insights

Platform design can counteract rating bias to improve fairness without significantly harming user experience.

Principles

• Rating systems can perpetuate biases against marginalized groups.
• A trade-off exists between user experience and provider fairness if anti-discrimination measures are not explicit.
• Proactive anti-discrimination design improves fairness and cooperation.

Method

An evolutionary game theoretical model simulates provider strategy dynamics (high/low effort) and platform recommender system interventions (kG, kM) to evaluate user experience and demographic parity ratio.

In practice

• Prioritize marginalized providers in search results (kM > 0).
• Tune search result demographics to reduce unfairness.
• Consider the impact of rating sensitivity (gamma) and bias (epsilon).

Topics

• Evolutionary Game Theory
• Recommender Systems
• Algorithmic Fairness
• Biased Ratings
• Digital Economy Platforms

Best for: AI Scientist, Research Scientist, Product Manager, AI Researcher, AI Ethicist, AI Product Manager

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.MA updates on arXiv.org.