Summary of Balancing Fairness and Accuracy in Data-restricted Binary Classification, by Zachary Mcbride Lazri et al.
Balancing Fairness and Accuracy in Data-Restricted Binary Classification
by Zachary McBride Lazri, Danial Dervovic, Antigoni Polychroniadou, Ivan Brugere, Dana Dachman-Soled, Min Wu
First submitted to arxiv on: 12 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (stat.ML)
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 A novel machine learning framework is proposed to tackle the challenge of sensitive information restriction in applications where data availability affects accuracy and fairness. The framework models the trade-off between these two aspects under four practical scenarios that dictate data access. Unlike prior works, this approach directly analyzes the optimal Bayesian classifier’s behavior on the underlying distribution constructed from the dataset itself. This enables formulation of multiple convex optimization problems to answer how a Bayesian classifier’s accuracy is affected by different data restricting scenarios while maintaining fairness. Experiments on three datasets demonstrate the framework’s utility in quantifying trade-offs among various fairness notions and their dependencies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new machine learning method helps solve a problem where some information is too sensitive to share. Imagine you’re trying to teach a computer to make decisions, but it can’t see all the facts because they’re private. This paper shows how to balance getting accurate answers with making fair decisions when there are restrictions on the data available. It does this by looking at how well a special kind of calculator works in different situations where it doesn’t have access to all the information. The researchers tested their method on three datasets and found that it can help us understand how different fairness standards work together. |
Keywords
* Artificial intelligence * Machine learning * Optimization
