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Financial decisions impact our lives, and thus everyone from the regulator to the consumer is interested in fair, sound, and explainable decisions.
Adversarially regularized autoencoders for generating discrete structures
Junbo Jake Zhao, Yoon Kim, Kelly Zhang, Alexander M Rush, and Yann LeCun · 1906
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Learning overhypotheses with hierarchical bayesian models
Charles Kemp, Amy Perfors, and Joshua B Tenenbaum · 2007
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The mythos of model interpretability
Zachory Lipton · 2016
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Why should i trust you? explaining the predictions of any classifier
Marco Tulio Ribeiro Ribeiro, Sameer Singh, and Carlos Guestrin · 2016
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Power to the people: The role of humans in interactive machine learning
S Amershi, Maya Cakmak, William Knox, and Todd Kulesza · 2017
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Global fintech investment robust on back of strong vc funding:kpmg
DigitalNewsAsia · 2017
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What does explainable ai really mean? a new conceptualization of perspectives
D Doran · 2017
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Towards a rigorous science of interpretable machine learning
Finale Doshi-Velez and Been Kim · 2017
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Accountability of ai under the law: The role of explanation
Finale Doshi-Velez and Mason Kortz · 2017
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Explainable artificial intelligence
Dave Gunning · 2017
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Deligan: Generative adversarial networks for diverse and limited data
Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, and R Venkatesh Babu · 2017
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The promise and peril of human evaluation for model interpretability
B Herman · 2017
Cited alongside, same era.
Causalgan: Learning causal implicit generative models with adversarial training
Murat Kocaoglu, Christopher Snyder, Alexandros G Dimakis, and Sriram Vishwanath · 2017
Cited alongside, same era.
Explainable ai: Beware of inmates running the asylum
T Millers · 2017
Do explanations make vqa models more predictable to a human?
and Prabhu Viraj Chandrasekaran, Arjun, Deshraj Yadav, Prithvijit Chattopadhyay, and Devi Parikh · 2018
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Machine learning and fico scores: An evolution in ml innovations that helps both lenders and consumers
FICO · 2018
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xai toolkit: Practical, explainable machine learning
Andy Flint, Arash Nourian, and Jari Koister · 2018
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How explainability is driving the future of artificial intelligence
Kyndi · 2018
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How do humans understand explanations from machine learning systems:an evaluation of the human-interpretability of explanation
Menaka Narayanan, Emily Chen, Jeffrey He, Been Kim, Sam Gershman, and Finale Doshi-Velez · 2018
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Multimodal explanations: Justifying decisions and pointing to the evidence
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Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
R. Selvaraju, A. Das, Vedantam, M. R., Cogswell, D. Parikh, and D. Batra · 2017
Cited alongside, same era.
D. Park, L. Hendricks, Z. Akata, A. Rohrbach, B. Schiele, T. Darrell, and M. Rohrbach · 2018
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Explainable ai driving business value through greater understanding
PWC · 2018
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