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DECIDING IN THE DARK:A Survey of The Current
Ecosystem of Explainability
Techniques
By Coco Sack 1
WHO AM I?
 AI2 Incubator Intern
 Yale freshman (Computer Science & Psychology major)
 Child of AI Boom
 As of recent, ...
3
WHO CARES ABOUT EXPLAINABILITY?
DATA-SCIENTISTS
To debug...
POLITICIANS
GDPR:
“a right to
explanation”
To protect the publ...
EXPLAINABILITY: A HOT TOPIC
5
???
6
77
88
DATA ANALYSIS
◂ Data must be complete, unique, credible, accurate, consistent, and
unbiased
◂ Errors or duplicates can und...
INTRINSICALLY INTERPRETABLE MODELS
1. Regression 2. Additive 3. Tree Graphs 4. Decision Rules
10
OUTPUT ANALYSIS TECHNIQUES
11
WHAT KIND OF MODELS DO THEY WORK ON?
12
WHAT KIND OF MODELS DO THEY WORK ON?
13
WHAT DOES IT OUTPUT?
14
WHAT DOES IT OUTPUT?
15
LOCAL VS. GLOBAL?
16
OTHER EXPLAINABILITY RESOURCES
General Surveys of Techniques:
A Survey of Methods For Explaining
Black Box Models (2018)
V...
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In this talk, Coco Sack talks about current explainability techniques in the field.

Presented on 08/21/2019

**These slides are from a talk given at Rsqrd AI. Learn more at rsqrdai.org**

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Rsqrd AI: A Survey of The Current Ecosystem of Explainability Techniques

  1. 1. DECIDING IN THE DARK:A Survey of The Current Ecosystem of Explainability Techniques By Coco Sack 1
  2. 2. WHO AM I?  AI2 Incubator Intern  Yale freshman (Computer Science & Psychology major)  Child of AI Boom  As of recent, an “Explainability Expert” 2
  3. 3. 3
  4. 4. WHO CARES ABOUT EXPLAINABILITY? DATA-SCIENTISTS To debug... POLITICIANS GDPR: “a right to explanation” To protect the public... EXECUTIVES 76% said lack of transparency was seriously impeding adoption To sell... CONSUMERS To trust... 4
  5. 5. EXPLAINABILITY: A HOT TOPIC 5
  6. 6. ??? 6
  7. 7. 77
  8. 8. 88
  9. 9. DATA ANALYSIS ◂ Data must be complete, unique, credible, accurate, consistent, and unbiased ◂ Errors or duplicates can undermine the model’s performance ◂ Outdated bias in the training data can re-entrench discrimination. You are saying, 'Here's the data, figure out what the behavior is.' That is an inherently fuzzy and statistical approach. The real challenge of deep learning is that it's not modeling, necessarily, the world around it. It's modeling the data it's getting. And that modeling often includes bias and problematic correlations.” -- Sheldon Fernandez, CEO of DarwinAI 9
  10. 10. INTRINSICALLY INTERPRETABLE MODELS 1. Regression 2. Additive 3. Tree Graphs 4. Decision Rules 10
  11. 11. OUTPUT ANALYSIS TECHNIQUES 11
  12. 12. WHAT KIND OF MODELS DO THEY WORK ON? 12
  13. 13. WHAT KIND OF MODELS DO THEY WORK ON? 13
  14. 14. WHAT DOES IT OUTPUT? 14
  15. 15. WHAT DOES IT OUTPUT? 15
  16. 16. LOCAL VS. GLOBAL? 16
  17. 17. OTHER EXPLAINABILITY RESOURCES General Surveys of Techniques: A Survey of Methods For Explaining Black Box Models (2018) Visual Analytics in Deep Learning (2018) Explaining Explanations (2019) Peeking Inside the Black Box (2018) Books and Presentations: Interpretable Machine Learning (by Christopher Molnar, 2019) XAI (by Dave Gunning, 2017) Explaining Explanations (2019) Unique Technical Papers: Generative Synthesis (Wong et al.) Golden Eye++ (Hendricks et al.) Grad-CAM (Selvaraju et al.) DeepLift (Shrikumar et al.) T-CAV (Kim et al.) Ethical/Political Reports: “Computer Says No” (by Ian Sample) “Why We Need to Open the Black Box” (by AJ Abdallat) “The Importance of Interpretable Machine learning” (by DJ Sakar) 17

In this talk, Coco Sack talks about current explainability techniques in the field. Presented on 08/21/2019 **These slides are from a talk given at Rsqrd AI. Learn more at rsqrdai.org**

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