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Found 7 papers, 2 papers with code
Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems
We discuss the evaluation of users' mental models of system logic.
Soliciting Human-in-the-Loop User Feedback for Interactive Machine Learning Reduces User Trust and Impressions of Model Accuracy
Our research investigates how the act of providing feedback can affect user understanding of an intelligent system and its accuracy.
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems
While it is also known that user trust can be influenced by first impressions with intelligent systems, our research explores the relationship between ordering bias and domain expertise when encountering errors in intelligent systems.
Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
The explanations generated by these simplified models, however, might not accurately justify and be truthful to the model.
XFake: Explainable Fake News Detector with Visualizations
In this demo paper, we present the XFake system, an explainable fake news detector that assists end-users to identify news credibility.
A Survey of Evaluation Methods and Measures for Interpretable Machine Learning
The need for interpretable and accountable intelligent system gets sensible as artificial intelligence plays more role in human life.
Human-Computer Interaction
A Human-Grounded Evaluation Benchmark for Local Explanations of Machine Learning
We demonstrate our benchmark's utility for quantitative evaluation of model explanations by comparing it with human subjective ratings and ground-truth single-layer segmentation masks evaluations.
