Summary of A Principled Approach For a New Bias Measure, by Bruno Scarone et al.
A Principled Approach for a New Bias Measure
by Bruno Scarone, Alfredo Viola, Renée J. Miller, Ricardo Baeza-Yates
First submitted to arxiv on: 20 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper addresses the growing concern of negative data bias in machine learning algorithms used for decision making across various domains. The authors highlight the need for awareness and quantification of bias, as existing measures lack consensus on how to measure bias, which can have harmful consequences for specific groups of people. The main contributions include the definition of Uniform Bias (UB), a new bias measure with a clear interpretation, and a systematic study characterizing flaws in existing measures using anti-employment discrimination rules. The authors also provide a framework for deriving mathematical formulas for bias measures based on algorithmic specifications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how machine learning algorithms can be biased and cause harm to specific groups of people. Researchers have been struggling to agree on how to measure this bias, which makes it hard to understand and fix the problem. The authors propose a new way to measure bias called Uniform Bias (UB), which is easy to understand and works well across different situations. They also show how their approach can be used to create a model that helps policymakers reduce bias in decision-making systems. |
Keywords
» Artificial intelligence » Machine learning
