This talk was presented by Mrs. Dorothea Wisemann, Department Head Cognitive Computing & Industry Solutions at IBM Resarch - Zurich, during Data Science Conference 4.0, as a keynote talk. More info about Data Science Conference: Website: http://datasciconference.com Instagram: https://www.instagram.com/datasciconf/ Facebook: https://www.facebook.com/DataSciConference/ Twitter: https://twitter.com/datasciconf Flickr: https://www.flickr.com/photos/data-science-conference

1. The Need for Explainable AI
—
Dr. Dorothea Wiesmann
Head of Cognitive Computing & Industry Solutions
IBM Research Zurich

2. The Need for Explainable AI

3. 3
Transistors Network Volume Data
Knowledge
1971
Moore’s Law
1995
Metcalfe’s Law
Today
Watson’s Law

4. Strategic imperatives: Developing core AI
AI will have enhanced
reasoning abilities and will
be widely distributed,
helping us make decisions
instantly.

5. Narrow AI:
Initial Value
Creation
5
2010 2015

6. 6
2010 2015
General AI:
Revolutionary
2050–Beyond
Narrow AI:
Initial Value
Creation

7. 7
2010 2015
General AI:
Revolutionary
2050–BeyondWe are here
Broad AI:
Disruptive and
Pervasive
Narrow AI:
Initial Value
Creation

8. Advancing Broad AI
Signal Comprehension:
From video and text to rich
human perception
Learning and Reasoning:
From scalable machine
learning to making a case
Interaction:
Understanding language,
tone, emotion and context
“A green bird sitting on top
of a bowl”

9. Watson Data & AI
Every
Every Business
and Enterprise
is Embracing AI
Leaders everywhere are
monetizing data and
developing strategies to
embed AI in business
Every Business
and Enterprise
is Embracing AI
Leaders everywhere are
monetizing data and
developing strategies to
embed AI in business
Financial Services Manufacturing Education Life SciencesLogistics

10. Technical R&D today: The three pillars
Theory/Knowledge
Experiment
Simulation

11. The way forward: Cognitive Discovery
Create technical area
specific knowledge space
from all relevant sources.
Use knowledge space to drastically
augment internal know-how &
modeling, focus on which experiment
is relevant, embed results in
knowledge base
Use inference on the
knowledge space &
simulation on the models
Cognitive Discovery
Drastically accelerate pace
of systematic discovery
and maximize ROI for R&D
Experimental
Results
Knowledge Inference &
Simulation
Evidence &
Experiments
Technical R&D Augmented

12. Goal:
• Manage
• Parse
• Annotate
• Train/learn
• Convert
PDF documents into a
semantic representation
Deep parsing of scientific papers
Takes about 30 seconds on a laptop, 20,000 papers/week

13. IBM RXN for Chemistry
Predicting the Outcome of Chemical Reactions

14. IBM RXN for Chemistry
Seq-2-Seq Model
Atoms as Letters, Molecules as Words
Source/
Input
Encoder
Interesting
Features
Decoder
Target/
Output
Schwaller et al.: Chem. Sci., 2018, 9, 6091-6098

15. IBM RXN
for Chemistry
Freely available now:
research.ibm.com/ai4chemistry
#RXNFORCHEMISTRY

16. AI Adoption Is Accelerating
of companies
believe that AI is key to
competitive advantage
94%
AI associated with CRM
activities will boost global
business revenue by
from 2017 to 2021
$1.1T
By 2019, of digital
transformation initiatives will use AI
services and by 2021, of
enterprise applications will use AI
75%
40%

17. AI Adoption Is Accelerating
…but there is also a set of unique challenges
Only 1 in 20
companies have
extensively
incorporated AI in
offerings or processes
Top reasons for lack of AI
Adoption
• Skills: Lack of requisite talent
to drive AI adoption
• Data: Only 19% respondents
strongly agreed that their
organizations understand the
data required to train AI
algorithms. Data used is not of
high quality or trusted
• Trust: Only 35% of IT and
Business decision makers had a
high level of trust in their own
organization's analytics. AI
insights not well integrated into
current processes
of companies
believe that AI is key to
competitive advantage
94%
AI associated with CRM
activities will boost global
business revenue by
from 2017 to 2021
$1.1T
By 2019, of digital
transformation initiatives will use AI
services and by 2021, of
enterprise applications will use AI
75%
40%

18. The Need for Explainable AI

19. Example Recommender Systems
Source: https://www.amazon.de/Zeller-14375-Katzen-K%C3%B6rbchen-Katzen-Korb-
Anthrazit/dp/B072MHBCP7/ref=sr_1_fkmr0_1?ie=UTF8&qid=1537183815&sr=8-1-fkmr0&keywords=Zeller+Present+Hai

20. Example Recommender Systems
Source: https://www.amazon.de/Zeller-14375-Katzen-K%C3%B6rbchen-Katzen-Korb-
Anthrazit/dp/B072MHBCP7/ref=sr_1_fkmr0_1?ie=UTF8&qid=1537183815&sr=8-1-fkmr0&keywords=Zeller+Present+Hai

21. High performance, Explainable/Interpretable Models
Gregoire Montavon et al., arXiv:1706.07979v1 [cs. LG] 24 Jun 2017
 Interpretability: An interpretation is the mapping of an abstract
concept (e.g. a predicted class) into a domain that the human can
make sense of e.g. through prototypes.
 Explainability: An explanation is the collection of features of the
interpretable domain, that have contributed for a given example to
produce a decision (e.g. classification or regression).

22. Interpreting DNN Models
D. Erhan, Y. Bengio, A. Courville, and
P. Vincent, Tech. Rep, 4323, 2009
 Concept: Building prototype in the
input domain that is interpretable
and representative of the abstract
learned concept.
 Method: Activation Maximization
(input pattern that produces a
maximum model response for a
quantity of interest) constraining the
optimization to generate only
realistic prototypes)
Anh Nguyen, Alexey Dosovitskiy, Jason
Yosinski, Thomas Brox, and Jeff Clune,
NIPS 2016

23. Improving Simple Models with Confidence Profiles
A. Dhurandhara, K. Shanmugamb, R. Lussc,
and P. Olsen, arXiv:1807.07506v1, 2018
 Concept: Transferring information
from a pre-trained deep neural
network with high accuracy to a
simpler interpretable model
 Method: Linear probes to generate
confidence scores for DNN.
Transfer by theoretically justified
weighting of samples during the
training of the simple model using
confidence scores of these
intermediate layers.

24. High performance, Explainable/Interpretable Models
Gregoire Montavon et al., arXiv:1706.07979v1 [cs. LG] 24 Jun 2017
 Interpretability: An interpretation is the mapping of an abstract
concept (e.g. a predicted class) into a domain that the human can
make sense of e.g. through prototypes.
 Explainability: An explanation is the collection of features of the
interpretable domain, that have contributed for a given example to
produce a decision (e.g. classification or regression).

25. Layerwise Relevance Propagation (LRP)
 Method: Beginning with the
“relevance” being
concentrated at the output
node in the graph, and then
iteratively “propagate” it
backwards through the
network
S. Bach, A. Binder, G. Montavon, F. Klauschen, K.-R. Müller,
W. Samek, PLoS ONE 10(7): e0130140

26. Local Interpretable Model-agnostic Explanations (LIME)
 Concept: Algorithm that can explain the predictions of any classifier in a faithful
way, by approximating it locally with an interpretable model.
Marco Ribeiro, Sameer Singh, and Carlos Guestrin. ACM
SIGKDD Intl. Conference on Knowledge Discovery and Data
Mining, 2016

27. Contrastive Explanations with
Pertinent Negatives
 Concept: Explain a classification with
pertinent positive features and pertinent
negative features
 Method: Given an input,
• find what should be minimally and
sufficiently present to justify its
classification and
• find what should be minimally and
necessarily absent
A. Dhurandhar, P.-Y. Chen, R. Luss, C.-C. Tu,
P. Ting, K. Shanmugam and P. Das,
arXiv:1802.07623v1, 2018

28. 28
IBM Cloud / Watson and Cloud Platform / © 2018 IBM Corporation
Thanks!
Q&A