Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples

📝 Original Info

• Title: Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples
• ArXiv ID: 2001.02271
• Date: 2020-01-13
• Authors: Chelsea M. Myers, Evan Freed, Luis Fernando Laris Pardo, Anushay Furqan, Sebastian Risi, Jichen Zhu

📝 Abstract

AI algorithms are not immune to biases. Traditionally, non-experts have little control in uncovering potential social bias (e.g., gender bias) in the algorithms that may impact their lives. We present a preliminary design for an interactive visualization tool CEB to reveal biases in a commonly used AI method, Neural Networks (NN). CEB combines counterfactual examples and abstraction of an NN decision process to empower non-experts to detect bias. This paper presents the design of CEB and initial findings of an expert panel (n=6) with AI, HCI, and Social science experts.

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