论文标题
通过交互式反事实示例揭示神经网络偏见对非专家
Revealing Neural Network Bias to Non-Experts Through Interactive Counterfactual Examples
论文作者
论文摘要
AI算法不能免疫偏见。传统上，非专家几乎无法控制可能影响其生活的算法中潜在的社会偏见（例如性别偏见）。我们为交互式可视化工具CEB提供了初步设计，以揭示常用的AI方法神经网络（NN）中的偏见。 CEB结合了反事实示例和NN决策过程的抽象，以赋予非专家检测偏见。本文介绍了与AI，HCI和社会科学专家的专家小组（n = 6）的CEB的设计和初步发现。
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.
