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Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values
30 June 2022
Zijie J. Wang
Alex Kale
Harsha Nori
P. Stella
M. Nunnally
Duen Horng Chau
Mihaela Vorvoreanu
J. W. Vaughan
R. Caruana
KELM
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Papers citing
"Interpretability, Then What? Editing Machine Learning Models to Reflect Human Knowledge and Values"
6 / 6 papers shown
Title
Misty: UI Prototyping Through Interactive Conceptual Blending
Yuwen Lu
Alan Leung
Amanda Swearngin
Jeffrey Nichols
Titus Barik
26
3
0
20 Sep 2024
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Clara Punzi
Roberto Pellungrini
Mattia Setzu
F. Giannotti
D. Pedreschi
25
5
0
09 Feb 2024
DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
Angelos Chatzimparmpas
Rafael M. Martins
A. Telea
Andreas Kerren
24
1
0
31 Mar 2023
Lessons from the Development of an Anomaly Detection Interface on the Mars Perseverance Rover using the ISHMAP Framework
Austin P. Wright
P. Nemere
A. Galvin
Duen Horng Chau
Scott Davidoff
24
6
0
14 Feb 2023
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Zijie J. Wang
Chudi Zhong
Rui Xin
Takuya Takagi
Zhi Chen
Duen Horng Chau
Cynthia Rudin
Margo Seltzer
33
14
0
19 Sep 2022
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
Caroline Linjun Wang
Bin Han
Bhrij Patel
Cynthia Rudin
FaML
HAI
59
84
0
08 May 2020
1