69 citations · 80 across the 3 of their papers we have counts for
3 papers
FeedLens: Polymorphic Lenses for Personalizing Exploratory Search over Knowledge Graphs
Harmanpreet Kaur, Doug Downey, Amanpreet Singh +3
The vast scale and open-ended nature of knowledge graphs (KGs) make exploratory search over them cognitively demanding for users. We introduce a new technique, polymorphic lenses,…
Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory
Harmanpreet Kaur, Eytan Adar, Eric Gilbert +1
Understanding how ML models work is a prerequisite for responsibly designing, deploying, and using ML-based systems. With interpretability approaches, ML can now offer explanations…
From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence
David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé +2
We take inspiration from the study of human explanation to inform the design and evaluation of interpretability methods in machine learning. First, we survey the literature on huma…