The document discusses various methods for explaining AI model predictions, including LIME and SHAP. LIME explains individual predictions by approximating them with an interpretable model, while SHAP connects game theory and local explanations by using Shapley values. The document also mentions educational projects on filtering academic papers and human-machine interactions, emphasizing the need for explainable user interfaces and agent strategy summarization.

1. Bridging the gap
between AI and UI

2. Thank you!

3. Thank you!

4. Jurassic Park

5. Jurassic Park vs Sarah Jessica Parker

6. What does it mean?

7. Not what I plan to say!

8. Matching Engine

9. Google Apple
?

10. Feedback
- Constructive
- Focused

11. Understand
Your
Model

12. “Why should I trust you?” - Explaining the Predictions of
Any Classifier
LIME: Local Interpretable Model-Agnostic Explanations

13. LIME
- Explains the model result
- Enhances User Trust
Supports Multi-class classifications, (example),
text documents, images, etc.
Packages for R, Python, etc.
As seen on PwC talk & workshop!

14. SHAP - NIPS`17
SHapley Additive exPlanations
“A Unified Approach to Interpreting
Model Predictions” - Lundberg & Lee
“Connects game theory
with local explanations”
SHAP Value - Feature importance as
an impact (effect) on the output

15. SHAP - NIPS`17
Unifies:
- LIME
- Shapley sampling values
- Shapley regression values
- DeepLIFT
.
.
.
better consistency with human
intuition
Simon - Craftworks

16. Keep-Current

17. Activities - Keep-Current Meetup
- Learning by doing
- Machine Learning Seminar - Monthly in Vienna
- Applied Machine Learning Course
- PyTorch, Fast.AI

18. Keep-Current
Educational Project
Filter newly released academic papers
Hybrid recommendation system
- Collaborative filtering
- Content Similarity
- User-Feedback (MOT)
- RNN

19. Methods
Topic extraction (LDA / LSI)
Document Vector Representation
- Citation Correlation Matrix Decomposition
- Paragraph Vectors
- ELMo / BERT - Attention layer
Similarity using Spotify ANNOY (Approximate Nearest Neighbours - Oh Yeah!)
Methods were covered by Alexandros Karatzoglou

20. User-Input
Key-words / topics Extraction
Semantic Similarity
Vector distance:
- cosine similarity
- Euclidean Distance
- Word Mover’s Distance
- PIP

21. NIPS 18
PIP - Pairwise Inner Product

22. NeurIPS 18
yesterday!
(While you were partying)
PIP - Pairwise Inner Product

24. “Augmented Intelligence”
(Simon Stiebellehner)
“Human Teaching”
(Shai Herz)

25. Human-Machine
Interactions

26. Human-Machine Interactions - Ofra Amir
PhD, Harvard University
Intelligent Interactive Systems
(advanced topics in information systems)
- Technion, Israel
AAMAS'18:
- HIGHLIGHTS: Summarizing Agent Behavior to
People
- Agent Strategy Summarization

28. Meh...

29. Explain results
- Keywords Contribution:
Positive / Negative
- Encourage User Feedback
Model adaptation
- Stronger Engagement

30. What’s in RNN Vector?
http://u.cs.biu.ac.il/~yogo/blackbox2018.pdf

31. What’s in RNN Vector?
http://u.cs.biu.ac.il/~yogo/blackbox2018.pdf
As mentioned by Vered Schwarz
deeper at DSI_UniWien - 12.12

32. Explainable AI
- Models are prune to make mistakes
- Interpretability to the rescue!
- Be Creative: Explainable UI
Future Research:
- Effective Agent Strategy Summary
- Expected Behavior under Different Conditions

33. ?

34. Thank You
Liad Magen
www.linkedin.com/in/liadmagen
www.github.com/keep-current
Keep-Current Meetup
Liad.magen @ gmail.com