While Deep Neural Networks (DNNs) are deriving the major innovations in
nearly every field through their powerful automation, we are also witnessing
the peril behind automation as a form of bias, such as automated racism, gender
bias, and adversarial bias. As the societal impact of DNNs grows, finding an
effective way to steer DNNs to align their behavior with the human mental model
has become indispensable in realizing fair and accountable models. We propose a
novel framework of Interactive Attention Alignment (IAA) that aims at realizing
human-steerable Deep Neural Networks (DNNs). IAA leverages DNN model
explanation method as an interactive medium that humans can use to unveil the
cases of biased model attention and directly adjust the attention. In improving
the DNN using human-generated adjusted attention, we introduce GRADIA, a novel
computational pipeline that jointly maximizes attention quality and prediction
accuracy. We evaluated IAA framework in Study 1 and GRADIA in Study 2 in a
gender classification problem. Study 1 found applying IAA can significantly
improve the perceived quality of model attention from human eyes. In Study 2,
we found using GRADIA can (1) significantly improve the perceived quality of
model attention and (2) significantly improve model performance in scenarios
where the training samples are limited. We present implications for future
interactive user interfaces design towards human-alignable AI.

提出了一种交互式注意力对齐（IAA）框架，旨在实现可操纵的深度神经网络（DNNs），其中利用 DNN 模型解释方法作为交互媒介，直接调整注意力以改善 DNN 的准确性和公正性，并提出了一个计算流程，即 GRADIA，可以同时最大化注意力质量和预测准确性。通过两项研究，发现这种框架可以显著提高模型准确性和公正性，为未来交互式用户界面设计提供了启示。