Fairer Prediction Models: Balance Accuracy and Fairness in Regression
Researchers built a simple way to make prediction models more fair, that works with common regression tools people already use.
The idea lets teams tune a single setting to trade a bit of accuracy for more fairness, and it runs fast so you can try many options without waiting.
You can watch how accuracy drops or holds up as fairness rises, and they put that change into a clear number called the Price of Fairness.
They tested the idea on six different collections of data, and the results shows sometimes fairness costs very little, other times it costs more — so every case is different.
This gives groups a clear way to compare choices, pick a point that feels right, and explain the cost in plain terms.
If you care about fair tech, this approach makes the trade-offs visible, easy to explore, and quick to use — so teams can make better choices for people, not just for scores.
Read article comprehensive review in Paperium.net:
A Convex Framework for Fair Regression
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