Summary of Achievable Fairness on Your Data with Utility Guarantees, by Muhammad Faaiz Taufiq et al.
Achievable Fairness on Your Data With Utility Guarantees
by Muhammad Faaiz Taufiq, Jean-Francois Ton, Yang Liu
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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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 Medium Difficulty summary: This research paper proposes a novel approach to address the fairness-accuracy trade-off in machine learning. The fairness-accuracy trade-off refers to the phenomenon where training models that minimize disparity across different sensitive groups often leads to diminished accuracy. The severity of this trade-off depends on dataset characteristics such as imbalances or biases, making it challenging to develop a uniform fairness requirement across diverse datasets. To tackle this challenge, the authors present a computationally efficient approach to approximate the fairness-accuracy trade-off curve tailored to individual datasets. This approach utilizes the You-Only-Train-Once (YOTO) framework and provides rigorous statistical guarantees. The methodology also includes a novel method for quantifying uncertainty in estimates, allowing practitioners to audit model fairness without making false conclusions due to estimation errors. The authors demonstrate the effectiveness of their approach through experiments on tabular, image, and language datasets, showing that it can reliably quantify optimum achievable trade-offs across various data modalities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research is about finding a balance between making sure machine learning models are fair and not sacrificing too much accuracy. Right now, if we want to make our models more fair, they might not be as good at getting things right. The problem is that the way we measure fairness can depend on the type of data we’re using. To solve this, scientists came up with a new way to calculate how fair and accurate our models are. This method helps us figure out what’s the best balance between fairness and accuracy for different types of data. By testing it on lots of different datasets, they showed that their approach can really help us make better choices when designing our models. |
Keywords
* Artificial intelligence * Machine learning
