Interpretable and Safe AI Research Landscape Map

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Protozoa

Szerző: Jenny Doriot

RISK MANAGEMENT

Szerző: Mindomo Team

Question 3: Restorative Justice in Prison

Szerző: Gillian Dickinson

Cell Phone Pro's and Con's

Szerző: Max Meints

Highlighted papers are key discussions and surveys of interpretable ML

Interpretable and Safe AI Research Landscape Map

Discussion Of Explanations

Explanation in AI

Z. Lipton, “The doctor just wont accept that!” in arXiv 2015;1711.08037v2, 2015.

C. Rudin, “Please stop explaining black box models for high-stakes decisions,” arXiv:1811.10154v2, 2018.

D. Doran, S. Schulz, and T. R. Besold, “What does explainable ai really mean? a new conceptualization of perspectives,” in arXiv:1710.00794v1 [cs.AI] 2 Oct 2017, 2017

Z. C. Lipton, “The mythos of model interpretability,”

T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,” in https://arxiv.org/abs/1706.07269, 2018.

Shane T. Mueller "Explanation in Human-AI Systems: A Literature Meta-Review Synopsis of Key Ideas and Publications and Bibliography for Explainable AI"

Philosophy

P. Lipton, “Contrastive explanation,” in Royal Institute of Philosophy Supplement 27:247-266, 1990.

Achinstein, “The nature of explanation,” in Oxford University Press, 1983

Grimm, “The goal of explanation,” in Studies in the History and Philosophy of Science A, 41(4), 337-344, 2010.

Requirements and needs

Safety

Legality

S. Wachter, B. Mittelstadt, and L. Floridi, “Why a right to explana-tion of automated decision-making does not exist in the general dataprotection regulation,” 2017.

R. Budishet al., “Accountability of ai under the law: The role of ex-planation,”

B. Goodman and S. Flaxman, “European union regulations on algorithmic decision-making and a ”right to explanation”,” AI Magazine, Vol 38, No 3, 2017, 2016.

Machine Learning

Applications

Autonomous Vehicles

Health Care

N. Tomaev, X. Glorot, and S. Mohamed, “A clinically applicable ap-proach to continuous prediction of future acute kidney injury,” 2019.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional net-works for biomedical image segmentation,” 2015.

A. Avati, K. Jung, S. Harman, L. Downing, A. Ng, and N. H. Shah, “Im-proving palliative care with deep learning,”

Interpretability techniques for ML models

Classifiers

L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele,and T. D. and, “Generating visual explanations,”

C. Otte, “Safe and interpretable machine learning a methodologicalreview,”

M. T. Ribeiro, S. Singh, and C. Guestrin,“why should i trust you? explaining the predictions of any classifier, ” https://arxiv.org/abs/1602.04938, 2016.

Model Agnostic

S. Wachter, B. D. Mittelstadt, and C. Russell, “Counterfactualexplanations without opening the black box: Automated decisions andthe GDPR,”CoRR, vol. abs/1711.00399, 2017

I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick,S. Mohamed, and A. Lerchner, “Learning basic visual concepts with aconstrained variational framework,”ICLR 2017, 2017.

H. Lakkaraju, E. Kamar, R. Caruana, and J. Leskovec, “Interpretableexplorable approximations of black box models,” inarXiv:1707.01154v1[cs.AI] 4 Jul 2017, 2017

P. W. Koh and P. Liang, “Understanding black-box predictions viainfluence functions,”ICML’17 Proceedings of the 34th InternationalConference on Machine Learning - Volume 70 Pages 1885-1894, 2017.11

Unsupervised Learning

M. T. Ribeiro,S. Singh,and C. Guestrin,“why shoulditrustyou?explainingthepredictionsofanyclassifier,”https://arxiv.org/abs/1602.04938, 2016

S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting modelpredictions,” inarXiv:1705.07874v2 [cs.AI] 25 Nov 2017, 2017

Supervised Learning

Z.C.Lipton,“Themythosofmodelinterpretability,”arXiv:1606.03490v3 [cs.LG], 2017

F. K. Dosilovi, M. Brci, and N. Hlupi, “Explainable artificial intelli-gence: A survey,”MIPRO 2018, May 21-25, 2018, Opatija Croatia,2018

Neural Nets

K. Simonyan, A. Vedaldi, and A. Zisserman, “Deep inside convolutionalnetworks: Visualising image classification models and saliency maps,”https://arxiv.org/abs/1312.6034, 2013

Q. shi Zhang and S. chun Zhu, “Visual interpretability for deep learning:a survey,”Frontiers of Information Technology Electronic Engineering,2018.

S. Sarkar, “Accuracy and interpretability trade-offs in machine learningapplied to safer gambling,” inCEUR Workshop Proceedings

L. H. Gilpin, D. Bau, B. Z. Yuan, A. Bajwa, M. Specter, and L. Kagal,“Explaining explanations: An overview of interpretability of machinelearning,”arXiv:1806.00069v3 [cs.AI] 3 Feb 2019, 2019

A. Avati, K. Jung, S. Harman, L. Downing, A. Ng, and N. H. Shah, “Im-proving palliative care with deep learning,”arXiv:1711.06402v1 [cs.CY]17 Nov 2017, 2017

C. Olah, L. Schubert, and A. Mordvintsev, “Feature visualization how neural networks build up their understanding of images

T. Zahavy, N. B. Zrihem, and S. Mannor, “Graying the black box:Understanding dqns

RL

L. A. Hendricks, Z. Akata, M. Rohrbach, J. Donahue, B. Schiele,and T. D. and, “Generating visual explanations,”arXiv:1603.08507v1[cs.CV] 28 Mar 2016, 2016

Safety Critical Systems

Assurance

Chiara, “A pattern for arguing the assurance of machine learning inmedical diagnosis systems,” inAssuring Autonomy International Pro-gramme, The University of York, York, U.K.

R. Ashmore, R. Calinescu, and C. Paterson, “Assuring the ma-chine learning lifecycle: Desiderata, methods, and challenges,” inhttps://arxiv.org/pdf/1905.04223.pdf, May 2019.

T. Kelly, “A systematic approach to safety case management.” inhttps://www-users.cs.york.ac.uk/tpk/04AE-149.pdf, 2003.

Surveys

Theory

S. Mohseni, N. Zarei, and E. D. Ragan, “A survey of evaluation methods and measures for interpretable machine learning

T. Miller, “Explanation in artificial intelligence: Insights from the social sciences,”

Interpretability Techniques

F. K. Dosilovi, M. Brci, and N. Hlupi, “Explainable artificial intelli-gence: A survey,”

Mohseni et al "Explainable Artificial Intelligence: A Survey."

O. Biran and C. Cotton, “Explanation and justification in machine learning

R. Ashmore, R. Calinescu, and C. Paterson, “Assuring the machine learning lifecycle

A. Adadi and M. Berrada, “Peeking inside the black-box"