Categories: All - assurance - responsibility - morality - transparency

by Rhys Ward 5 years ago

191

Explainable and Safe AI Research Landscape Map

The research landscape in the field of explainable and safe artificial intelligence (AI) encompasses various key themes and methodologies aimed at elucidating and ensuring the reliability of AI systems.

Explainable and Safe AI Research Landscape Map

Explainable and Safe AI Research Landscape Map

Theory of Explanations

Definitions
Transparency vs Explanations

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

Philosophy
Good Explanations

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

Responsibility
Morality

Requirements and needs

Technical
Safety
User Needs
Legality
R. Budishet al., “Accountability of ai under the law: The role of ex-planation,”
GDPR (Goodman & Flaxman)

Machine Learning

Applications
Autonomous Vehicles
Health Care

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

ML Methods
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,”

Unsupervised Learning
Supervised Learning
Neural Nets

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

Safety Critical Systems

Assurance
Safety Cases

Interpretability methods

Evaluation of Explanation
Transparancy
Explanations
Global
Local

Saliency maps

T. Zahavy, N. B. Zrihem, and S. Mannor, “Graying the black box:Understanding dqns,”https://arxiv.org/abs/1602.02658, 2016.

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.

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,”
Ex. 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"