Designing a Direct Feedback Loop between Humans and Convolutional Neural
  Networks through Local Explanations
Tong Steven Sun Yuyang Gao Shubham Khaladkar Sijia Liu Liang Zhao Young-Ho Kim Sungsoo Ray Hong
The local explanation provides heatmaps on images to explain how
Convolutional Neural Networks (CNNs) derive their output. Due to its visual
straightforwardness, the method has been one of the most popular explainable AI
(XAI) methods for diagnosing CNNs. Through our formative study (S1), however,
we captured ML engineers' ambivalent perspective about the local explanation as
a valuable and indispensable envision in building CNNs versus the process that
exhausts them due to the heuristic nature of detecting vulnerability. Moreover,
steering the CNNs based on the vulnerability learned from the diagnosis seemed
highly challenging. To mitigate the gap, we designed DeepFuse, the first
interactive design that realizes the direct feedback loop between a user and
CNNs in diagnosing and revising CNN's vulnerability using local explanations.
DeepFuse helps CNN engineers to systemically search "unreasonable" local
explanations and annotate the new boundaries for those identified as
unreasonable in a labor-efficient manner. Next, it steers the model based on
the given annotation such that the model doesn't introduce similar mistakes. We
conducted a two-day study (S2) with 12 experienced CNN engineers. Using
DeepFuse, participants made a more accurate and "reasonable" model than the
current state-of-the-art. Also, participants found the way DeepFuse guides
case-based reasoning can practically improve their current practice. We provide
implications for design that explain how future HCI-driven design can move our
practice forward to make XAI-driven insights more actionable.