Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples. (arXiv:2001.02271v1 [cs.HC])

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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