Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples
Human-Computer Interaction
2020-01-13 v2 Artificial Intelligence
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.
Cite
@article{arxiv.2001.02271,
title = {Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples},
author = {Chelsea M. Myers and Evan Freed and Luis Fernando Laris Pardo and Anushay Furqan and Sebastian Risi and Jichen Zhu},
journal= {arXiv preprint arXiv:2001.02271},
year = {2020}
}