Summary of Fairness Incentives in Response to Unfair Dynamic Pricing, by Jesse Thibodeau et al.
Fairness Incentives in Response to Unfair Dynamic Pricing
by Jesse Thibodeau, Hadi Nekoei, Afaf Taïk, Janarthanan Rajendran, Golnoosh Farnadi
First submitted to arxiv on: 22 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores how AI methods can help policymakers achieve fair demand representation in markets with dynamic pricing. The authors are concerned about demand fairness concerns, which arise from profit-maximizing firms’ use of dynamic pricing strategies that may not reflect the underlying population’s demographics. To address this issue, they design a simulated economy and introduce a dynamic social planner (SP) to generate corporate taxation schedules that incentivize firms to adopt fair pricing behaviors. They then formulate the SP’s learning problem as a multi-armed bandit, contextual bandit, and full reinforcement learning (RL) problem, evaluating welfare outcomes for each scenario. The authors also develop FairReplayBuffer to ensure uniform sampling of experiences across a discretized fairness space. Their results show that deploying a learned tax and redistribution policy improves social welfare compared to a fairness-agnostic baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence can help make sure that people from different groups are represented fairly in markets where prices change over time. Right now, companies use pricing strategies that might not be fair because they’re trying to make as much money as possible. To fix this problem, the authors created a pretend economy and a special planner to come up with rules for taxing companies so they’ll start being more fair. They then tested different ways of making these decisions using AI, like playing games or learning from experiences. Their results show that using AI to make better decisions about taxes can actually help people in the long run. |
Keywords
» Artificial intelligence » Reinforcement learning
