Summary of Towards Standardizing Ai Bias Exploration, by Emmanouil Krasanakis et al.
Towards Standardizing AI Bias Exploration
by Emmanouil Krasanakis, Symeon Papadopoulos
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY); Human-Computer Interaction (cs.HC)
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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 A novel mathematical framework for evaluating fairness in AI systems is presented. The framework distills existing measures of bias into building blocks, enabling the creation of new combinations to address a wide range of fairness concerns. This includes classification and recommendation differences across multiple sensitive attributes, such as gender and race intersections. The proposed FairBench Python library facilitates systematic exploration of potential bias concerns, generalizing existing concepts and providing a foundation for extensible evaluation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Researchers are trying to make artificial intelligence (AI) systems fairer by addressing biases that can occur in different situations. Currently, there is no easy way to measure all these biases at once. In this paper, the authors create a mathematical framework that breaks down existing bias measures into smaller parts, allowing them to combine them in new ways to cover more fairness concerns. They also release a Python library called FairBench that makes it easier for others to explore and address potential biases. |
Keywords
» Artificial intelligence » Classification
