Summary of Striking a Balance in Fairness For Dynamic Systems Through Reinforcement Learning, by Yaowei Hu et al.
Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning
by Yaowei Hu, Jacob Lear, Lu Zhang
First submitted to arxiv on: 12 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| 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 addresses the issue of fairness in machine learning models operating in dynamic systems, where sequential decisions are made and the underlying distribution of features or user behavior may shift. The authors model this dynamic system using a Markov Decision Process (MDP) and propose an algorithmic framework that integrates various fairness considerations with reinforcement learning. The framework includes both pre-processing and in-processing approaches to ensure balance between traditional fairness notions, long-term fairness, and utility. Case studies demonstrate the effectiveness of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a world where machines make decisions for us, but we want those decisions to be fair. This is especially important when the situation keeps changing. In this paper, researchers explore how to make sure machine learning models are fair even when things change over time. They use a special type of math called Markov Decision Processes (MDPs) to understand these dynamic systems. The goal is to create a balance between being fair now and being fair in the long run, while still making good decisions. The researchers tested their approach with three different scenarios and showed that it works well. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
