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Interpretability, Then What? Editing Machine Learning Models to Reflect
  Human Knowledge and Values

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"

4 / 4 papers shown
Title
Misty: UI Prototyping Through Interactive Conceptual Blending
Misty: UI Prototyping Through Interactive Conceptual Blending
Yuwen Lu
Alan Leung
Amanda Swearngin
Jeffrey Nichols
Titus Barik
24
3
0
20 Sep 2024
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
AI, Meet Human: Learning Paradigms for Hybrid Decision Making Systems
Clara Punzi
Roberto Pellungrini
Mattia Setzu
F. Giannotti
D. Pedreschi
15
5
0
09 Feb 2024
DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate
  Decision Stumps
DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumps
Angelos Chatzimparmpas
Rafael M. Martins
A. Telea
Andreas Kerren
16
1
0
31 Mar 2023
In Pursuit of Interpretable, Fair and Accurate Machine Learning for
  Criminal Recidivism Prediction
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction
Caroline Linjun Wang
Bin Han
Bhrij Patel
Cynthia Rudin
FaML
HAI
57
83
0
08 May 2020
1