Visual analysis of discrimination in machine learning

Qianwen Wang, Zhenhua Xu, Zhutian Chen, Yong Wang, Shixia Liu, Huamin Qu

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

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.

Original languageEnglish (US)
Article number9222272
Pages (from-to)1470-1480
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume27
Issue number2
DOIs
StatePublished - Feb 2021
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 1995-2012 IEEE.

Keywords

  • Data Visualization
  • Discrimination
  • Machine Learning

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