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European Union regulations on algorithmic decision-making and a "right
  to explanation"
v1v2v3 (latest)

European Union regulations on algorithmic decision-making and a "right to explanation"

28 June 2016
B. Goodman
Seth Flaxman
    FaMLAILaw
ArXiv (abs)PDFHTML

Papers citing "European Union regulations on algorithmic decision-making and a "right to explanation""

28 / 528 papers shown
Granger-causal Attentive Mixtures of Experts: Learning Important
  Features with Neural Networks
Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural Networks
Patrick Schwab
Djordje Miladinovic
W. Karlen
CML
330
60
0
06 Feb 2018
Plan Explanations as Model Reconciliation -- An Empirical Study
Plan Explanations as Model Reconciliation -- An Empirical Study
Tathagata Chakraborti
S. Sreedharan
Sachin Grover
S. Kambhampati
150
48
0
03 Feb 2018
Evaluating neural network explanation methods using hybrid documents and
  morphological agreement
Evaluating neural network explanation methods using hybrid documents and morphological agreement
Nina Pörner
Benjamin Roth
Hinrich Schütze
229
9
0
19 Jan 2018
A Computational Model of Commonsense Moral Decision Making
A Computational Model of Commonsense Moral Decision Making
Richard Kim
Max Kleiman-Weiner
A. Abeliuk
E. Awad
Sohan Dsouza
J. Tenenbaum
Iyad Rahwan
219
60
0
12 Jan 2018
Interpretability Beyond Feature Attribution: Quantitative Testing with
  Concept Activation Vectors (TCAV)
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
Been Kim
Martin Wattenberg
Justin Gilmer
Carrie J. Cai
James Wexler
F. Viégas
Rory Sayres
FAtt
498
2,111
0
30 Nov 2017
The Doctor Just Won't Accept That!
The Doctor Just Won't Accept That!
Zachary Chase Lipton
FaML
103
103
0
20 Nov 2017
Semantic Structure and Interpretability of Word Embeddings
Semantic Structure and Interpretability of Word Embeddings
Lutfi Kerem Senel
Ihsan Utlu
Veysel Yücesoy
Aykut Koç
Tolga Çukur
171
115
0
01 Nov 2017
How Algorithmic Confounding in Recommendation Systems Increases
  Homogeneity and Decreases Utility
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
A. Chaney
Brandon M Stewart
Barbara E. Engelhardt
CML
380
355
0
30 Oct 2017
Training Feedforward Neural Networks with Standard Logistic Activations
  is Feasible
Training Feedforward Neural Networks with Standard Logistic Activations is Feasible
Emanuele Sansone
F. D. De Natale
106
4
0
03 Oct 2017
Learning Functional Causal Models with Generative Neural Networks
Learning Functional Causal Models with Generative Neural Networks
Hugo Jair Escalante
Sergio Escalera
Xavier Baro
Isabelle M Guyon
Umut Güçlü
Marcel van Gerven
CMLBDL
383
111
0
15 Sep 2017
Explainable Artificial Intelligence: Understanding, Visualizing and
  Interpreting Deep Learning Models
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models
Wojciech Samek
Thomas Wiegand
K. Müller
XAIVLM
295
1,346
0
28 Aug 2017
Beyond the technical challenges for deploying Machine Learning solutions
  in a software company
Beyond the technical challenges for deploying Machine Learning solutions in a software company
I. Flaounas
68
13
0
08 Aug 2017
Axiomatic Characterization of Data-Driven Influence Measures for
  Classification
Axiomatic Characterization of Data-Driven Influence Measures for Classification
Jakub Sliwinski
Martin Strobel
Yair Zick
TDI
171
14
0
07 Aug 2017
Interpretable Active Learning
Interpretable Active Learning
R. L. Phillips
K. H. Chang
Sorelle A. Friedler
FAtt
115
31
0
31 Jul 2017
Interpreting Classifiers through Attribute Interactions in Datasets
Interpreting Classifiers through Attribute Interactions in Datasets
A. Henelius
Kai Puolamäki
Antti Ukkonen
FAtt
134
40
0
24 Jul 2017
Optimal Cooperative Inference
Optimal Cooperative Inference
Scott Cheng-Hsin Yang
Yue Yu
A. Givchi
Pei Wang
Wai Keen Vong
Patrick Shafto
206
22
0
24 May 2017
Detecting Statistical Interactions from Neural Network Weights
Detecting Statistical Interactions from Neural Network Weights
Michael Tsang
Dehua Cheng
Yan Liu
288
210
0
14 May 2017
Learning Certifiably Optimal Rule Lists for Categorical Data
Learning Certifiably Optimal Rule Lists for Categorical Data
E. Angelino
Nicholas Larus-Stone
Daniel Alabi
Margo Seltzer
Cynthia Rudin
358
201
0
06 Apr 2017
Towards Moral Autonomous Systems
Towards Moral Autonomous Systems
V. Charisi
Louise A. Dennis
Michael Fisher
Robert Lieck
A. Matthias
Marija Slavkovik
Janina Sombetzki
A. Winfield
Roman V. Yampolskiy
117
66
0
14 Mar 2017
Understanding Black-box Predictions via Influence Functions
Understanding Black-box Predictions via Influence Functions
Pang Wei Koh
Abigail Z. Jacobs
TDI
516
3,287
0
14 Mar 2017
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAIFaML
695
4,525
0
28 Feb 2017
EVE: Explainable Vector Based Embedding Technique Using Wikipedia
EVE: Explainable Vector Based Embedding Technique Using WikipediaJournal of Intelligence and Information Systems (JIIS), 2017
M. A. Qureshi
Derek Greene
197
34
0
22 Feb 2017
Simple rules for complex decisions
Simple rules for complex decisions
Jongbin Jung
Connor Concannon
Ravi Shroff
Sharad Goel
D. Goldstein
CML
152
109
0
15 Feb 2017
Ethical Considerations in Artificial Intelligence Courses
Ethical Considerations in Artificial Intelligence CoursesThe AI Magazine (AI Magazine), 2017
Emanuelle Burton
J. Goldsmith
Sven Koenig
B. Kuipers
Nicholas Mattei
T. Walsh
152
149
0
26 Jan 2017
Interpreting Finite Automata for Sequential Data
Interpreting Finite Automata for Sequential Data
Christian A. Hammerschmidt
S. Verwer
Qin Lin
R. State
99
16
0
21 Nov 2016
On the Safety of Machine Learning: Cyber-Physical Systems, Decision
  Sciences, and Data Products
On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data ProductsBig Data (BD), 2016
Kush R. Varshney
H. Alemzadeh
238
242
0
05 Oct 2016
Learning Optimized Risk Scores
Learning Optimized Risk Scores
Berk Ustun
Cynthia Rudin
700
96
0
01 Oct 2016
The Mythos of Model Interpretability
The Mythos of Model Interpretability
Zachary Chase Lipton
FaML
690
4,078
0
10 Jun 2016
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