Presented at the 31st ACM User Interface Software and Technology Symposium (UIST), 2018. Paper: https://www.ischool.utexas.edu/~ml/papers/nguyen-uist18.pdf

1. Believe it or not: Designing a Human-AI
Partnership for Mixed-Initiative Fact-Checking
Joint work with
An Thanh Nguyen (UT), Byron Wallace (Northeastern), & more!
Matt Lease
School of Information @mattlease
University of Texas at Austin ml@utexas.edu
Slides:
slideshare.net/mattlease

2. “The place where people & technology meet”
~ Wobbrock et al., 2009
“iSchools” now exist at over 65 universities around the world
www.ischools.org
What’s an Information School?
2Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

3. Information Literacy
National Information Literacy Awareness Month,
US Presidential Proclamation, October 1, 2009.
“Though we may know how to find the information
we need, we must also know how to evaluate it.
Over the past decade, we have seen a crisis of
authenticity emerge. We now live in a world where
anyone can publish an opinion or perspective, whether
true or not, and have that opinion amplified…”
3Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

4. “Truthiness”
“Truthiness is tearing apart our country... It used to
be everyone was entitled to their own opinion, but
not their own facts. But that’s not the case anymore.”
– Stephen Colbert (Jan. 25, 2006)
4Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

5. Journalist Fact-Checking
5Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

6. Danny Sullivan,
April 7, 2017
6Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

7. Crowdsourced Fact Checking
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8. Automated Fact-Checking
8Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

9. Fake News Challenge
9Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

10. 10Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

11. 11Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

12. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Challenges
• Fair, Accountable, & Transparent (AI)
– Why trust “black box” classifier?
– How do we reason about potential bias?
– Do people really only want to know true vs. false?
– How to integrate human knowledge/experience?
• Joint AI + Human Reasoning, Correct Errors, Personalization
• How to design strong Human + AI Partnerships?
– Horvitz, CHI’99: mixed-initiative design
– Dove et al., CHI’17 “Machine Learning As a Design Material”
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13. MemeBrowser (Ryu et al., ACM HyperText’12)
http://odyssey.ischool.utexas.edu/mb
13Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking

14. • Crowdsourced stance labels
– Hybrid AI + Human (near real-time) Prediction
• Joint model of stance, veracity, & annotators
– Interaction between variables
– Interpretable
• Source on github
Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Nguyen et al., AAAI’18
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15. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
This Work
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Demo!

16. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
This Work
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http://fcweb.pythonanywhere.com

17. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Primary Interface
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18. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Source Reputation
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19. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
System Architecture
• Google Search API
• Two logistic regression models
– Stance (Ferreira & Vlachos ’16) w/ same features
• average accuracy > 70% but variable over claims
– Veracity (Popat et al. ‘17)
– Scikit-learn, L1 regularization, Liblinear solver, &
default parameters
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20. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Data: Train & Test
Emergent (Ferreira & Vlachos ’16)
Accuracy of prediction models
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21. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 1: Setup
• 2 Groups: Control vs. System
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22. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 1: Setup
• 2 Groups: Control vs. System
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23. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 1: Setup
• 2 Groups: Control vs. System
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24. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 1: Setup
• 2 Groups: Control vs. System
– 113 participants (58 control, 55 system) – MTurk
• 1. Asked to predict claim veracity
– Likert: Def. False, Prob. F., neutral, Prob. True, Def. T.
– Error: distance
• For a (definitely) false claim, PF -> error=1, DT -> error=4
• 2. Shown model’s claim prediction
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25. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 1: Setup
• 2 Groups: Control vs. System
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26. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 1: Setup
• 2 Groups: Control vs. System
26

27. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 1: Setup
• 2 Groups: Control vs. System
– 113 participants (58 control, 55 system) – MTurk
• 1. Asked to predict claim veracity
– Likert: Def. False, Prob. F., neutral, Prob. True, Def. T.
– Error: distance
• For a (definitely) false claim, PF -> error=1, DT -> error=4
• 2. Shown model’s claim prediction
– Asked if they want to change their answer?
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28. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Before seeing model’s claim prediction
• “System” group:
– claims 1-2: > avg. prediction error
– claim 4: < error
– claims 3, 5: only small differences
• Human accuracy in claim prediction roughly
follows model’s accuracy in stance prediction
– i.e., helped when model correct, hurt when not
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29. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
After seeing model’s claim prediction
• “System” group:
– Smaller changes for errors
– change answers > “control” group
• Human accuracy in claim prediction roughly
follows model’s accuracy in veracity prediction
– i.e., helped when model correct, hurt when not
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30. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Study 1: Statistical Significance (1 of 2)
• Mixed-effects Generalized Linear Model (GLM)
• Before seeing model’s claim predictions
– CSP: # correct stance predictions seen
– WSP: # wrong stance predictions seen
– 2-tail test includes unlikely possibility that seeing
correct stance predictions increases human error
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31. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Study 1: Statistical Significance (2 of 2)
• Mixed-effects Generalized Linear Model (GLM)
• After seeing model’s claim predictions
– CSP: # correct stance predictions seen
– WSP: # wrong stance predictions seen
– 2-tail test includes unlikely possibility that seeing
correct stance predictions increases human error
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32. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 2: Setup
• 2 Groups: Control vs. Slider
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33. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
User Study 2: Setup
• 2 Groups: Control vs. Slider
– 109 participants (51 control, 58 slider) – MTurk
• 1. Asked to predict claim veracity
– Likert: Def. False, Prob. F., neutral, Prob. True, Def. T.
and indicate confidence in prediction
– (-20,+20) score range: accuracy x confidence
• eg, a correct answer with 75% confidence -> 20x75%=15
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34. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
• “Slider” group:
– Claims 1-3: > score on average
– Claims 4-5: < first quartiles, but medians same
• Some participants negatively impacted by slider
– No statistically significant difference on average
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User Study 2: Results

35. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Discussion & Future Work
• Fact Checking & IR (Lease, DESIRES’18)
– How to diversify search results for controversial topics?
– Information evaluation (eg, vaccination & autism)
• Potential harm as well as good
– Potential added confusion, data / algorithmic bias
– Potential for personal “echo chamber”
– Adversarial applications
• Future Work
– Making personalization more visible
– Collaborative use, small and big
• 1st author Nguyen looking for a postdoc!
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36. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Conclusion
• Fact-checking more than black-box prediction
– Interaction, exploration, trust
• We proposed a mixed-initiative human + AI
partnership for fact-checking
– Back-end AI + front-end interface/interaction
– Support AI + human collaboration
– Fair, Accountable, & Transparent (FAT) AI
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37. Matt Lease (UT Austin) • Believe it or not: Designing a Human-AI Partnership for Mixed-Initiative Fact-Checking
Thank You!
Slides: slideshare.net/mattlease
Lab: ir.ischool.utexas.edu
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