# Balancing Profit and Fairness in Risk-Based Pricing Markets

arXiv: https://arxiv.org/abs/2506.00140 · HTML: https://mlsanity.com/paper/2506.00140/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 5 of 5)

- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies](https://mlsanity.com/paper/2004.13332/) (2020)
- [Fairness, Welfare, and Equity in Personalized Pricing](https://mlsanity.com/paper/2012.11066/) (2020)
- [Reinforcement Learning for Fair Dynamic Pricing](https://mlsanity.com/paper/1803.09967/) (2018)
- [Individual Fairness in Feature-Based Pricing for Monopoly Markets](https://mlsanity.com/paper/2202.12844/) (2022)

## Abstract (arXiv)

Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and consumer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's local demographic gap implicitly bounds the global opt-out disparity, motivating firm-level penalties. Building on this insight we introduce textttMarketSim -- an open-source, scalable simulator of heterogeneous consumers and profit-maximizing firms -- and train a reinforcement learning (RL) social planner (SP) that selects a bracketed fairness-tax while remaining close to a simple linear prior via an L1 regularizer. The learned policy is thus both transparent and easily interpretable. In two empirically calibrated markets, i.e., U.S. health-insurance and consumer-credit, our planner simultaneously raises demand-fairness by up to 16% relative to unregulated Free Market while outperforming a fixed linear schedule in terms of social welfare without explicit coordination. These results illustrate how AI-assisted regulation can convert a competitive social dilemma into a win-win equilibrium, providing a principled and practical framework for fairness-aware market oversight.