Humans to the Rescue: Troubleshooting AI Systems with Human-in-the-loop

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This document discusses using human-in-the-loop techniques to troubleshoot AI systems. It outlines how humans can help identify issues like biases, blind spots and areas for improvement by analyzing the system's execution data, outputs, and failures. The document also notes that complex challenges will require collaborative efforts between humans and AI due to limitations in the systems and the presence of "unknown unknowns."

Humans to the Rescue: Troubleshooting
AI Systems with Human-in-the-loop
Ece Kamar
Senior Researcher, Microsoft Research AI
eckamar@microsoft.com

AI and the Crowd
training data
accuracy
test data

Hybrid Intelligence
Human
Intelligence
AI Systems

Power of the Hybrid
[Courtesy of Murray Campbell]

Troubleshooting of ML Systems
training data
accuracy
test data
query
system
response
execution
data
In the lab
In the wild
What is the performance in the wild?
How does the system fail?
Why does the system fail?
How the system can be improved?

Biases in ML
[Lakkaraju, K., Caruana, Horvitz; AAAI 2017]

Where do Blind Spots Come From?
M
cats
dogs
cat
(conf = 0.96)
Unknown unknowns: Data points with confident but incorrect predictions.
Blind-spots: Feature spaces with high concentration of unknown unknowns

Blind-spots Detection
execution data
Beat the Machine
[Attenberg, Ipeirotis, Provost, 2011]
Exploration of Unknown Unknowns
[Lakkaraju, K., Caruana, Horvitz, 2011]
Step 1:
Descriptive
Space
Partitioning
execution data
Step 2:
Multi-armed
Bandit
based
Exploration

Challenge
Possible fixes
for each
component
Limited development time
Where to invest
development time for
biggest impact?

Human-assisted troubleshooting methodology
system
outputComponent
1
Component
2
Component
3
I/OI/O
Evaluation
Failures
Fixes
[Nushi, K., Kossmann, Horvitz, 2011]

Complex Issues
Fairness Biases
TransparencyResponsibility
Good
vs. Bad
Policy & Law

Complex challenges
require collective efforts
No AI is perfect

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1. Humans to the Rescue: Troubleshooting AI Systems with Human-in-the-loop Ece Kamar Senior Researcher, Microsoft Research AI eckamar@microsoft.com

2. Exciting Times

3. AI and the Crowd training data accuracy test data

4. Power of Data [Banko&Brill, 2001]

5. In the Wild

6. In the Wild

7. Hybrid Intelligence Human Intelligence AI Systems

8. AI Applied to Critical Domains

9. Power of the Hybrid [Courtesy of Murray Campbell]

10. Troubleshooting of ML Systems training data accuracy test data query system response execution data In the lab In the wild What is the performance in the wild? How does the system fail? Why does the system fail? How the system can be improved?

11. Biases in ML [Lakkaraju, K., Caruana, Horvitz; AAAI 2017]

12. Biases in ML [Lakkaraju, K., Caruana, Horvitz; AAAI 2017]

13. Biases in ML [Lakkaraju, K., Caruana, Horvitz; AAAI 2017]

14. Where do Blind Spots Come From? M cats dogs cat (conf = 0.96) Unknown unknowns: Data points with confident but incorrect predictions. Blind-spots: Feature spaces with high concentration of unknown unknowns

15. Blind-spots Detection execution data Beat the Machine [Attenberg, Ipeirotis, Provost, 2011] Exploration of Unknown Unknowns [Lakkaraju, K., Caruana, Horvitz, 2011] Step 1: Descriptive Space Partitioning execution data Step 2: Multi-armed Bandit based Exploration

16. Troubleshooting Complex Systems

17. Challenge Possible fixes for each component Limited development time Where to invest development time for biggest impact?

18. Human-assisted troubleshooting methodology system outputComponent 1 Component 2 Component 3 I/OI/O Evaluation Failures Fixes [Nushi, K., Kossmann, Horvitz, 2011]

19. Complex Issues Fairness Biases TransparencyResponsibility Good vs. Bad Policy & Law

20. Complex challenges require collective efforts No AI is perfect