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Explanation in Artificial Intelligence: Insights from the Social
  Sciences
v1v2v3 (latest)

Explanation in Artificial Intelligence: Insights from the Social Sciences

22 June 2017
Tim Miller
    XAI
ArXiv (abs)PDFHTML

Papers citing "Explanation in Artificial Intelligence: Insights from the Social Sciences"

36 / 1,336 papers shown
State of the Art in Fair ML: From Moral Philosophy and Legislation to
  Fair Classifiers
State of the Art in Fair ML: From Moral Philosophy and Legislation to Fair Classifiers
Elias Baumann
J. L. Rumberger
FaML
135
4
0
20 Nov 2018
Economics of Human-AI Ecosystem: Value Bias and Lost Utility in
  Multi-Dimensional Gaps
Economics of Human-AI Ecosystem: Value Bias and Lost Utility in Multi-Dimensional Gaps
Daniel Muller
98
1
0
15 Nov 2018
TED: Teaching AI to Explain its Decisions
TED: Teaching AI to Explain its Decisions
Michael Hind
Dennis L. Wei
Murray Campbell
Noel Codella
Amit Dhurandhar
Aleksandra Mojsilović
Karthikeyan N. Ramamurthy
Kush R. Varshney
226
114
0
12 Nov 2018
Contrastive Explanation: A Structural-Model Approach
Contrastive Explanation: A Structural-Model Approach
Tim Miller
CML
215
187
0
07 Nov 2018
Progressive Disclosure: Designing for Effective Transparency
Progressive Disclosure: Designing for Effective Transparency
Aaron Springer
Ling Huang
135
20
0
06 Nov 2018
"I had a solid theory before but it's falling apart": Polarizing Effects
  of Algorithmic Transparency
"I had a solid theory before but it's falling apart": Polarizing Effects of Algorithmic Transparency
Aaron Springer
S. Whittaker
78
8
0
06 Nov 2018
Explaining Explanations in AI
Explaining Explanations in AI
Brent Mittelstadt
Chris Russell
Sandra Wachter
XAI
335
723
0
04 Nov 2018
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison
  of Machine Learning and Logit Models
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models
Xilei Zhao
X. Yan
Alan Yu
Pascal Van Hentenryck
136
25
0
04 Nov 2018
Compositional Attention Networks for Interpretability in Natural
  Language Question Answering
Compositional Attention Networks for Interpretability in Natural Language Question Answering
Selvakumar Murugan
Suriyadeepan Ramamoorthy
Vaidheeswaran Archana
Malaikannan Sankarasubbu
125
3
0
30 Oct 2018
Learning with Interpretable Structure from Gated RNN
Learning with Interpretable Structure from Gated RNN
Bo-Jian Hou
Zhi Zhou
AI4CE
246
80
0
25 Oct 2018
What can AI do for me: Evaluating Machine Learning Interpretations in
  Cooperative Play
What can AI do for me: Evaluating Machine Learning Interpretations in Cooperative Play
Shi Feng
Jordan L. Boyd-Graber
HAI
281
139
0
23 Oct 2018
Shedding Light on Black Box Machine Learning Algorithms: Development of
  an Axiomatic Framework to Assess the Quality of Methods that Explain
  Individual Predictions
Shedding Light on Black Box Machine Learning Algorithms: Development of an Axiomatic Framework to Assess the Quality of Methods that Explain Individual Predictions
Milo Honegger
127
44
0
15 Aug 2018
Techniques for Interpretable Machine Learning
Techniques for Interpretable Machine Learning
Mengnan Du
Ninghao Liu
Helen Zhou
FaML
474
1,192
0
31 Jul 2018
Contrastive Explanations for Reinforcement Learning in terms of Expected
  Consequences
Contrastive Explanations for Reinforcement Learning in terms of Expected Consequences
J. V. D. Waa
J. Diggelen
K. Bosch
Mark Antonius Neerincx
OffRL
161
114
0
23 Jul 2018
Machine Learning Interpretability: A Science rather than a tool
Machine Learning Interpretability: A Science rather than a tool
Abdul Karim
Avinash Mishra
M. A. Hakim Newton
A. Sattar
105
7
0
18 Jul 2018
Explainable Security
Explainable Security
Luca Vigano
Daniele Magazzeni
SILM
128
79
0
11 Jul 2018
A Theory of Diagnostic Interpretation in Supervised Classification
A Theory of Diagnostic Interpretation in Supervised Classification
Anirban Mukhopadhyay
FaMLFAtt
53
1
0
26 Jun 2018
xGEMs: Generating Examplars to Explain Black-Box Models
xGEMs: Generating Examplars to Explain Black-Box Models
Shalmali Joshi
Oluwasanmi Koyejo
Been Kim
Joydeep Ghosh
MLAU
175
41
0
22 Jun 2018
Towards a Grounded Dialog Model for Explainable Artificial Intelligence
Towards a Grounded Dialog Model for Explainable Artificial Intelligence
Prashan Madumal
Tim Miller
F. Vetere
L. Sonenberg
120
34
0
21 Jun 2018
Interpretable to Whom? A Role-based Model for Analyzing Interpretable
  Machine Learning Systems
Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
Richard J. Tomsett
Dave Braines
Daniel Harborne
Alun D. Preece
Supriyo Chakraborty
FaML
235
182
0
20 Jun 2018
Agent-Mediated Social Choice
Agent-Mediated Social Choice
Umberto Grandi
72
7
0
19 Jun 2018
Teaching Meaningful Explanations
Teaching Meaningful Explanations
Noel Codella
Michael Hind
Karthikeyan N. Ramamurthy
Murray Campbell
Amit Dhurandhar
Kush R. Varshney
Dennis L. Wei
Aleksandra Mojsilović
FAttXAI
211
7
0
29 May 2018
Semantic Explanations of Predictions
Semantic Explanations of Predictions
Freddy Lecue
Jiewen Wu
FAtt
60
11
0
27 May 2018
Explainable Recommendation: A Survey and New Perspectives
Explainable Recommendation: A Survey and New Perspectives
Zelong Li
Xu Chen
XAILRM
473
1,011
0
30 Apr 2018
A review of possible effects of cognitive biases on the interpretation
  of rule-based machine learning models
A review of possible effects of cognitive biases on the interpretation of rule-based machine learning models
Tomáš Kliegr
Š. Bahník
Johannes Furnkranz
280
119
0
09 Apr 2018
The Challenge of Crafting Intelligible Intelligence
The Challenge of Crafting Intelligible Intelligence
Daniel S. Weld
Gagan Bansal
165
253
0
09 Mar 2018
On Cognitive Preferences and the Plausibility of Rule-based Models
On Cognitive Preferences and the Plausibility of Rule-based Models
Johannes Furnkranz
Tomáš Kliegr
Heiko Paulheim
LRM
218
76
0
04 Mar 2018
Manipulating and Measuring Model Interpretability
Manipulating and Measuring Model Interpretability
Forough Poursabzi-Sangdeh
D. Goldstein
Jake M. Hofman
Jennifer Wortman Vaughan
Hanna M. Wallach
364
765
0
21 Feb 2018
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior
  Explanations
Hierarchical Expertise-Level Modeling for User Specific Robot-Behavior Explanations
S. Sreedharan
Siddharth Srivastava
S. Kambhampati
100
19
0
19 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
Visual Analytics in Deep Learning: An Interrogative Survey for the Next
  Frontiers
Visual Analytics in Deep Learning: An Interrogative Survey for the Next Frontiers
Fred Hohman
Minsuk Kahng
Robert S. Pienta
Duen Horng Chau
OODHAI
255
584
0
21 Jan 2018
Network Analysis for Explanation
Network Analysis for Explanation
Hiroshi Kuwajima
Masayuki Tanaka
FAtt
75
3
0
07 Dec 2017
Toward Foraging for Understanding of StarCraft Agents: An Empirical
  Study
Toward Foraging for Understanding of StarCraft Agents: An Empirical Study
Sean Penney
Jonathan Dodge
Claudia Hilderbrand
Andrew Anderson
Logan Simpson
Margaret Burnett
144
35
0
21 Nov 2017
Artificial Intelligence as Structural Estimation: Economic
  Interpretations of Deep Blue, Bonanza, and AlphaGo
Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGoEconometrics Journal (JE), 2017
Mitsuru Igami
118
34
0
30 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
388
111
0
15 Sep 2017
Deep Reinforcement Learning: An Overview
Deep Reinforcement Learning: An Overview
Yuxi Li
OffRLVLM
959
1,742
0
25 Jan 2017
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