Explaining the Gap: Visualizing One's Predictions Improves Recall and Comprehension of Data

By Yea-Seul Kim, Katharina Reinecke, Jessica R. Hullman. 2017

129 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jennifer Wortman Vaughan
2. Hanna M. Wallach
3. Forough Poursabzi-Sangdeh
4. Daniel G. Goldstein
5. Jake M. Hofman
6. Q. Liao
7. Chenhao Tan
8. Ming Yin

In Computer Vision and Pattern Recognition

1. Hanna M. Wallach
2. Daniel G. Goldstein
3. Jennifer Wortman Vaughan
4. Jake M. Hofman

Citations in the corpus

• Manipulating and Measuring Model Interpretability (2018, positive): “Indeed, this idea is supported by recent research, which found that eliciting predictions and presenting feedback is beneficial for people’s memory and comprehension of data points [52].”