Visual Analysis of Discrimination in Machine Learning.

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ID: 171485
2020
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Ranked #12 of 32 articles by views in ieee transactions on visualization and computer graphics

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Abstract
The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis. To reveal detailed information on algorithmic discrimination, DiscriLens identifies a collection of potentially discriminatory itemsets based on causal modeling and classification rules mining. By combining an extended Euler diagram with a matrix-based visualization, we develop a novel set visualization to facilitate the exploration and interpretation of discriminatory itemsets. A user study shows that users can interpret the visually encoded information in DiscriLens quickly and accurately. Use cases demonstrate that DiscriLens provides informative guidance in understanding and reducing algorithmic discrimination.
Reference Key
wang2020visualieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wang, Qianwen;Xu, Zhenhua;Chen, Zhutian;Wang, Yong;Liu, Shixia;Qu, Huamin;
Journal ieee transactions on visualization and computer graphics
Year 2020
DOI
10.1109/TVCG.2020.3030471
URL
Keywords
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