Summary of Balancing the Scales: Reinforcement Learning For Fair Classification, by Leon Eshuijs et al.
Balancing the Scales: Reinforcement Learning for Fair Classification
by Leon Eshuijs, Shihan Wang, Antske Fokkens
First submitted to arxiv on: 15 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Computers and Society (cs.CY)
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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 research explores the use of Reinforcement Learning (RL) to address bias in imbalanced classification tasks. The study focuses on embedding fairness into the training process by adjusting reward functions to mitigate bias. Three popular RL algorithms are adapted and employed within the contextual multi-armed bandit framework, demonstrating a novel approach to tackling bias. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a new way of learning called Reinforcement Learning (RL) to make sure classification models aren’t biased. Bias can happen when data is imbalanced, which means some groups have much more information than others. The researchers took three well-known RL methods and made them work for this problem. They did this by creating a special framework that helps the model learn how to be fair. |
Keywords
* Artificial intelligence * Classification * Embedding * Reinforcement learning
