Sensible AI: Re-imagining Interpretability and Explainability using Sensemaking Theory

no code implementations • 10 May 2022 • Harmanpreet Kaur, Eytan Adar, Eric Gilbert, Cliff Lampe

We use an application of sensemaking in organizations as a template for discussing design guidelines for Sensible AI, AI that factors in the nuances of human cognition when trying to explain itself.

From Human Explanation to Model Interpretability: A Framework Based on Weight of Evidence

1 code implementation • 27 Apr 2021 • David Alvarez-Melis, Harmanpreet Kaur, Hal Daumé III, Hanna Wallach, Jennifer Wortman Vaughan

We take inspiration from the study of human explanation to inform the design and evaluation of interpretability methods in machine learning.

Interpretable Machine Learning