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Machine learning (ML) is being applied to a diverse and ever-growing set of domains.
The Effects of a False Allegation of Child Sexual Abuse on an Intact Middle Class Family
D. Richardson · 1990
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A Nested Model for Visualization Design and Validation
T. Munzner · 2009
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Human-Centred Machine Learning
M. Gillies, R. Fiebrink, A. Tanaka, J. Garcia, F. Bevilacqua, A. Heloir, F. Nunnari, W. Mackay, S. Amershi, B. Lee, N. d’Alessandro, J. Tilmanne, T. Kulesza, and B. Caramiaux · 2016
Earlier work this paper cites.
Lifetime Prevalence of Investigating Child Maltreatment Among US Children
H. Kim, C. Wildeman, M. Jonson-Reid, and B. Drake · 2016
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Interacting with Predictions: Visual Inspection of Black-box Machine Learning Models
J. Krause, A. Perer, and K. Ng · 2016
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The Mythos of Model Interpretability
Z. C. Lipton · 2016
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Towards A Rigorous Science of Interpretable Machine Learning
F. Doshi-Velez and B. Kim · 2017
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ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
M. Kahng, P. Y. Andrews, A. Kalro, and D. H. Chau · 2017
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A Unified Approach to Interpreting Model Predictions
S. M. Lundberg and S.-I. Lee · 2017
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Automated Vehicles for Safety
National Highway Traffic Safety Administration (NHTSA) · 2017
Cited alongside, same era.
LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in Recurrent Neural Networks
H. Strobelt, S. Gehrmann, H. Pfister, and A. M. Rush · 2017
Cited alongside, same era.
Developing Predictive Models to Support Child Maltreatment Hotline Screening Decisions: Allegheny County Methodology and Implementation
R. Vaithianathan, E. Putnam-Hornstein, N. Jiang, P. Nand, and T. Maloney · 2017
Cited alongside, same era.
Can an Algorithm Tell When Kids Are in Danger?
D. Hurley · 2018
Cited alongside, same era.
RetainVis: Visual Analytics with Interpretable and Interactive Recurrent Neural Networks on Electronic Medical Records
B. C. Kwon, M.-J. Choi, J. T. Kim, E. Choi, Y. B. Kim, S. Kwon, J. Sun, and J. Choo · 2018
Cited alongside, same era.
Gamut: A Design Probe to Understand How Data Scientists Understand Machine Learning Models
F. Hohman, A. Head, R. Caruana, R. DeLine, and S. M. Drucker · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C. Rudin · 2019
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explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning
T. Spinner, U. Schlegel, H. Schäfer, and M. El-Assady · 2019
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Implementing a Child Welfare Decision Aide in Douglas County
R. Vaithianathan, H. Dinh, A. Kalisher, C. Kithulgoda, E. Kulick, M. Mayur, A. Ning, D. Benavides Prado, and E. Putnam-Hornstein · 2019
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Designing Theory-Driven User-Centric Explainable AI
D. Wang, Q. Yang, A. Abdul, and B. Y. Lim · 2019
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S. M. Lundberg, B. Nair, M. S. Vavilala, M. Horibe, M. J. Eisses, T. Adams, D. E. Liston, D. K.-W. Low, S.-F. Newman, J. Kim, and S.-I. Lee · 2018
Cited alongside, same era.
Perturbation-Based Explanations of Prediction Models
M. Robnik-Šikonja and M. Bohanec · 2018
Cited alongside, same era.
Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models
J. Zhang, Y. Wang, P. Molino, L. Li, and D. S. Ebert · 2018
Cited alongside, same era.
All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously
A. Fisher, C. Rudin, and F. Dominici · 2019
Cited alongside, same era.
L. Xu, M. Skoularidou, A. Cuesta-Infante, and K. Veeramachaneni · 2019
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Child Maltreatment 2018: Summary of Key Findings
Children’s Bureau · 2020
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Machine Learning Glossary: Fairness
Google · 2020
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How Cost-Benefit Analysis Process Is Performed
W. Kenton · 2021
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