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Found 6 papers, 1 papers with code
Pyreal: A Framework for Interpretable ML Explanations
Users in many domains use machine learning (ML) predictions to help them make decisions.
Lessons from Usable ML Deployments and Application to Wind Turbine Monitoring
Through the applications of our lessons to this task, we hope to demonstrate the potential real-world impact of usable ML in the renewable energy domain.
The Need for Interpretable Features: Motivation and Taxonomy
Through extensive experience developing and explaining machine learning (ML) applications for real-world domains, we have learned that ML models are only as interpretable as their features.
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks.
Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making
Machine learning (ML) is being applied to a diverse and ever-growing set of domains.
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making
As machine learning (ML) models are increasingly being employed to assist human decision makers, it becomes critical to provide these decision makers with relevant inputs which can help them decide if and how to incorporate model predictions into their decision making.
