Welcome to XAIES, the
expert platform for explainable AI solutions and systems.
LARGE RESOURCE OF
CURATED AND UPDATED INFORMATION ON XAI. PART 2.
Beyond XAI ?
https://www.ayasdi.com/beyond-explainability-ai-transparency/
Beyond Explainability - AI Transparency - AyasdiAI
It is now well understood that in order to make Artificial
Intelligence broadly useful, it is critical that humans can interact with and
have confidence in the algorithms that are being used. This observation has led
to the development of the notion of explainable AI (sometimes called XAI).
---------------------------------------------- -----------------------
Model Cards for
Model Reporting
https://arxiv.org/pdf/1810.03993.pdf
---------------------------------------------- -----------------------
GUIDE TO
INTERPRETABLE MACHINE LEARNING
https://www.topbots.com/interpretable-machine-learning/
If you cant explain it simply, you dont understand it well enough.
Albert Einstein Disclaimer:
---------------------------------------------- -----------------------
Explainability vs
interpretability
https://bdtechtalks.com/2020/07/27/black-box-ai-models/
---------------------------------------------- -----------------------
Explainable
anatomical shape
Spiral: Explainable anatomical shape analysis through deep
hierarchical generative models
https://arxiv.org/abs/1907.00058
SPIRAL.IMPERIAL.AC.UK
---------------------------------------------- -----------------------
Interpretable
policy derivation for reinforcement learning based on evolutionary feature
synthesis
LINK.SPRINGER.COM
https://link.springer.com/article/10.1007/s40747-020-00175-y
Reinforcement learning based on the deep neural network has attracted
much attention and has been widely used in real-world applications. However,
the black-box property limits its usage from applying in high-stake areas, such
as manufacture and healthcare.
---------------------------------------------- -----------------------
Self-explainable AI
https://bdtechtalks.com/2020/06/15/self-explainable-artificial-intelligence/
The case for self-explainable AI
Scientist Daniel Elton discusses why we need artificial intelligence
models that can explain their decisions by themselves as humans do.
---------------------------------------------- -----------------------
Evolution of
Classifier Confusion on the Instance Level
ARXIV.ORG: https://arxiv.org/abs/2007.11353
---------------------------------------------- -----------------------
Deep meta-learning
XAI
Explainable Artificial Intelligence (xAI)
Approaches and Deep Meta-Learning Models | IntechOpen
The explainable artificial intelligence (xAI)
is one of the interesting issues that has emerged
recently. Many researchers are trying to deal with the subject with different
dimensions and interesting results that have come out.
---------------------------------------------- -----------------------
Explaining Deep
Neural Networks using Unsupervised Clustering
https://arxiv.org/pdf/2007.07477.pdf
ARXIV.ORG
---------------------------------------------- -----------------------
Interactive Studio
for Explanatory Model Analysis, This is an R package.
https://cran.r-project.org/web/packages/modelStudio/modelStudio.pdf
CRAN.R-PROJECT.ORG
---------------------------------------------- -----------------------
Automated Reasoning
for Explainable AI
http://kocoon.gforge.inria.fr/slides/marques-silva.pdf
KOCOON.GFORGE.INRIA.FR
---------------------------------------------- -----------------------
The
"first" XAI libraries fusion
https://blog.fiddler.ai/2020/07/fiddler-captum-collaborate-on-explainable-ai/
BLOG.FIDDLER.AI
Fiddler Labs
AI with trust, visibility, and insightts
built in. Fiddler is a breakthrough AI engine with explainability
at its heart.
---------------------------------------------- -----------------------
AI perspective on
understanding and meaning
BDTECHTALKS.COM
https://bdtechtalks.com/2020/07/13/ai-barrier-meaning-understanding/
AIs struggle to reach understanding and meaning
Computer scientist Melanie Mitchell breaks down the key elements that
could allow artificial intelligence algorithms to grasp the "meaning"
of things.
---------------------------------------------- -----------------------
Robust Decision
Tree
https://link.springer.com/chapter/10.1007/978-3-030-50153-2_36
Robust Predictive-Reactive Scheduling: An Information-Based Decision
Tree Model
LINK.SPRINGER.COM
In this paper we introduce a proactive-reactive approach to deal with uncertain
scheduling problems.
---------------------------------------------- -----------------------
Yellowbrick directly from Scikit
SCIKIT-YB.ORG
Yellowbrick: Machine Learning Visualization Yellowbrick
v1.1 documentation
Yellowbrick extends the Scikit-Learn API to make model
selection and hyperparameter tuning easier. Under the
hood, its using Matplotlib.
---------------------------------------------- -----------------------
Levels of XAI
framework
https://link.springer.com/chapter/10.1007/978-3-030-51924-7_6
---------------------------------------------- -----------------------
Decision Theory
Meets Explainable AI. A CIU without the HAP !
https://link.springer.com/chapter/10.1007/978-3-030-51924-7_4
LINK.SPRINGER.COM
Decision Theory Meets Explainable AI
Explainability has been a core research topic in AI for decades and therefore it is
surprising that the current concept of Explainable AI (XAI) seems to have been
launched as late as 2016.
---------------------------------------------- -----------------------
ExplainX.ai
https://github.com/explainX/explainx
GITHUB.COM
Explain any Black-Box Machine Learning Model with explainX:
Fast, Scalable & State-of-the-art Explainable AI Platform. - explainX/explainx
---------------------------------------------- -----------------------
Explainable 3D
Convolutional Neural Networks by Learning Temporal Transformations
DEEPAI.ORG
In this paper we introduce the temporally factorized 3D convolution
(3TConv) as an interpretable alternative to the regular 3D convolutions.
---------------------------------------------- -----------------------
XAI package DALEX
https://github.com/ModelOriented/DALEX
GITHUB.COM
moDel Agnostic Language for Exploration and eXplanation
- ModelOriented/DALEX
---------------------------------------------- -----------------------
AIMLAI, Submission
deadline: Jul 22, 2020
https://project.inria.fr/aimlai/
PROJECT.INRIA.FR
Advances in Interpretable Machine Learning and Artificial Intelligence
(AIMLAI)
---------------------------------------------- -----------------------
CONSAC: Robust
Multi-Model Fitting by Conditional Sample Consensus
https://arxiv.org/pdf/2001.02643.pdf
also code
available: https://github.com/fkluger/consac
fkluger/consac
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus - fkluger/consac
---------------------------------------------- -----------------------
The four dimensions
of contestable AI diagnostics- A patient-centric approach to explainable AI
SCIENCEDIRECT.COM
https://www.sciencedirect.com/science/article/pii/S0933365720301330
The problem of the explainability of AI
decision-making has attracted considerable attention in recent years.
---------------------------------------------- -----------------------
When Explanations
Lie
https://arxiv.org/abs/1912.09818
(code page not found on my computer !)
ARXIV.ORG
When Explanations Lie: Why Many Modified BP Attributions Fail
---------------------------------------------- -----------------------
Attack to Explain
Deep Representation
OPENACCESS.THECVF.COM
---------------------------------------------- -----------------------
Funny title from
Google: Neural Networks Are More Productive
Teachers Than Human Raters 🙂, the paper
is as you might expect related to knowledge distillation from a black box. It
is accepted at CVPR taking place this week !
https://arxiv.org/pdf/2003.13960.pdf
ARXIV.ORG
---------------------------------------------- -----------------------
InterpretML from Microsoft
https://github.com/interpretml/interpret
GITHUB.COM
Fit interpretable models. Explain blackbox
machine learning. - interpretml/interpret
---------------------------------------------- -----------------------
SK-MOEFS: A Library
in Python for Designing Accurate and Explainable Fuzzy Models
LINK.SPRINGER.COM
SK-MOEFS: A Library in Python for Designing Accurate and Explainable
Fuzzy Models
Recently, the explainability of Artificial
Intelligence (AI) models and algorithms is becoming an important requirement in
real-world applications.
---------------------------------------------- -----------------------
Fooling LIME and
SHAP
https://arxiv.org/abs/1911.02508
ARXIV.ORG
Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation
Methods
---------------------------------------------- -----------------------
Explainable
cooperative machine learning
https://link.springer.com/article/10.1007/s13218-020-00632-3
LINK.SPRINGER.COM
eXplainable Cooperative Machine Learning with NOVA
In the following article, we introduce a novel workflow, which we
subsume under the term explainable cooperative machine learning and show its
practical application in a data annotation and model training tool called NOVA.
---------------------------------------------- -----------------------
XAI research job in
Rome
https://euraxess.ec.europa.eu/jobs/527048
---------------------------------------------- -----------------------
Neural Graph
Learning
STORAGE.GOOGLEAPIS.COM
---------------------------------------------- -----------------------
If you want to play
around, maybe earn some money:
https://www.innocentive.com/ar/challenge/browse?categoryName=Biology
INNOCENTIVE.COM
InnoCentive Challenge Center
---------------------------------------------- -----------------------
LIMEtree>>>>> https://arxiv.org/pdf/2005.01427.pdf
---------------------------------------------- -----------------------
Explainable AI Through Combination of Deep Tensor and Knowledge Graph
FUJITSU.COM
---------------------------------------------- -----------------------
Master thesis in
Quantifying the Performance of Explainability
Algorithms, University of Waterloo, 2020
https://uwspace.uwaterloo.ca/bitstream/handle/10012/15922/Lin_ZhongQiu.pdf?sequence=5&isAllowed=y
UWSPACE.UWATERLOO.CA
---------------------------------------------- -----------------------
XAI by Topological
Hierarchical Decomposition
https://math.osu.edu/events/topology-geometry-and-data-seminar-ryan-kramer
also the paper:
https://arxiv.org/abs/1811.10658
Topology, Geometry and Data Seminar - Ryan Kramer
MATH.OSU.EDU
---------------------------------------------- -----------------------
A very simple
manner to image XAI related to the way our brain thinks
https://hbr.org/2017/05/linear-thinking-in-a-nonlinear-world
---------------------------------------------- -----------------------
XAI for COVID-19 classification,
actually a RF
https://www.medrxiv.org/node/82227.external-links.html
MEDRXIV.ORG
An Interpretable Machine Learning Framework for Accurate Severe vs
Non-severe COVID-19 Clinical Type Classification
Effectively and efficiently diagnosing COVID-19 patients with accurate
clinical type is essential to achieve optimal outcomes of the patients as well
as reducing the risk of overloading the healthcare system.
---------------------------------------------- -----------------------
More Python XAI tools !
https://pyss3.readthedocs.io/en/latest/
Welcome to PySS3s documentation! PySS3 0.5.9 documentation
PYSS3.READTHEDOCS.IO
PySS3 is a Python package that allows you to work with The SS3
Classification Model in a very straightforward, interactive and visual way.
---------------------------------------------- -----------------------
XAI Critics !
---------------------------------------------- -----------------------
Interpreting Interpretability !
http://www-personal.umich.edu/~harmank/Papers/CHI2020_Interpretability.pdf
WWW-PERSONAL.UMICH.EDU
---------------------------------------------- -----------------------
gshap 0.0.3, latest released !
https://pypi.org/project/gshap/
gshap from PYPI.ORG
A technique in explainable AI for answering broader questions in
machine learning.
---------------------------------------------- -----------------------
Machine
learning-based XAI
https://ieeexplore.ieee.org/document/9007737
IEEEXPLORE.IEEE.ORG
Explainable Machine Learning for Scientific Insights and Discoveries -
IEEE Journals & Magazine
---------------------------------------------- -----------------------