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AI for Social Good:
The Essential Role of Human Machine Partnership
MILIND TAMBE
Founding Co-director, Center for Artificial Intelligence in Society (CAIS)
University of Southern California
tambe@usc.edu
Co-Founder, Avata Intelligence
USC Center for Artificial Intelligence in Society (CAIS.USC.EDU)
7/20/2017 3
Mission Statement: Advancing AI research driven by…
Grand Challenges of Social Work
▪ Ensure healthy development for all youth
▪ Close the health gap
▪ Stop family violence
▪ Advance long and productive lives
▪ End homelessness
▪ Achieve equal opportunity and justice
…
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Overview of CAIS Project Areas
AI Assistants for Low Resource Communities
▪ Social networks: Spread HIV information, influence maximization
▪ Real-world pilot tests: Big improvements
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Overview of CAIS Project Areas
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▪ Machine learning/planning: Predicting poaching spots, patrols
▪ Real-world: Uganda, South Asia…
AI Assistants for Conservation
▪ Game theory: security resource optimization
▪ Real-world: US Coast Guard, US Federal Air Marshals Service…
Overview of CAIS Project Areas
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AI Assistants for Public Safety and Security
Lesson Learned:Essential Nature of Human-Machine Partnership
▪ Building decision aids/assistants: Humans focus on their expertise
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▪ Low Resource Communities
▪ Public Safety and Security
▪ Wildlife Conservation
▪ Partnership at individual and organization level:
AI Assistant: HEALER
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AI Assistant for HIV Prevention:Using Social Networks to Spread HIV Information
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Challenge: Adaptive selection in Uncertain Network
K = 5
1st time step
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137/20/2017
Challenge: Adaptive selection in Uncertain Network
K = 5
2nd time step
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Challenge 3 : Adaptive selection
K = 5
3rd time step
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NO LONGER A SINGLE SHOT
DECISION PROBLEM
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Creating an Adaptive Policy: “POMDP” [2015]
▪ Homeless shelters – sequentially select nodes under uncertainty
➢ Policy driven by observations about edges
Action
Choose youth for
intervention
Observation: Which edges exist?
Adaptive Policy
POMDP POLICY
Generation
Actual social
Network in the
real world:
Real Networks – Simulation Results [2016-2017]
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0
10
20
30
40
50
60
70
80
90
Venice Hollywood
Ind
irec
t In
flu
ence
Degree CR PSINET HEALER-1 HEALER-2
Pilot Tests
with 170 Homeless Youth [2017]
Recruited youths:
Preliminary network —> HEALER
Bring 4 youth for training, get edge data —> HEALER
Bring 4 youth for training, get edge data —> HEALER
Bring 4 youth for training
HEALER HEALER++ DEGREE CENTRALITY
62 56 55
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Results: Pilot Studies
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0
20
40
60
80
100
HEALER HEALER++ Degree
Percent of non-Peer Leaders
Informed Not Informed
0
20
40
60
80
100
HEALER HEALER++ Degree
Informed Non-Peer Leaders Who Started Testing for HIV
Testing Non-Testing
Analysis: Pilot Studies
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0
5
10
15
20
25
HEALER HEALER++ Degree
% of edges between peer leaders
0
20
40
60
80
100
120
HEALER HEALER++ Degree
% Coverage of communities in 1st stage
AI Assistant: HEALER
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Next Steps: AI Assistant for HIV Prevention
▪ 900 youth study begun at three locations in Los Angeles
➢ 300 enrolled in HEALER/HEALER++
➢ 300 enrolled in no condition
➢ 300 in Degree centrality
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“Picking youth as peer leaders was
changing their self esteem and the
sense of confidence….”
Eric Rice
Overview of CAIS Project Areas
AI Assistants for Low Resource Communities
▪ Substance abuse, suicide prevention…
▪ Modeling gang violence, matching homeless and homes…
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AI Assistant for Protecting Wildlife in Uganda
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Data from Queen Elizabeth National Park, Uganda
AI Assistant for Poacher behavior prediction
Number of poaching attacks over 12 years: ~1000
How likely is an
attack on
a grid Square
Ranger patrol
frequency
Animal density
Distance to
rivers / roads
Area habitat
Area slope
…
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0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
L&L
Uniform Random SVM CAPTURE Decision Tree Our Best Model
Poacher Behavior Prediction
Poacher Attack Prediction
Results from 2015
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Real-world Deployment: (1 month)
Real-world Deployment: Results
▪ Two 9 sq KM patrol areas: Predicted hot spots with infrequent patrols
▪ Poached Animals: Poached elephant
▪ Snaring: 1 elephant snare roll
▪ Snaring: 10 Antelope snares
Historical Base Hit
RateOur Hit Rate
Average: 0.73 3
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0
0.02
0.04
0.06
0.08
0.1
0.12
High (1) Low (2)
Cat
ch p
er
Un
it E
ffo
rt
Experiment Group
Real-world Deployment: Field Test 2 (6 months)
▪ Catch Per Unit Effort (CPUE)
➢Unit Effort = km walked
➢Our high CPUE: 0.11
➢Our low CPUE: 0.01
Historical CPUE: 0.04
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Field Test Side Effects:Queen Elizabeth National Park
▪ Rangers followed poachers’ trail; ambushed camp
➢ Arrested one (of 7) poachers
➢ Confiscated 10 wire snares, cooking pot, hippo
meat, timber harvesting tools.
▪ Pursuit of poachers
▪ Signs of road building, fires, illegal fishing
Building AI Assistants:Human Machine Partnership in “AI for Social Good”
▪ Humans focus on their expertise, AI assistants on theirs:
➢ E.g., social workers interact with youth, AI assistants on selecting youth
▪ Lessons in Building Assistants:
➢ Right task division for humans vs machines
➢ Right adjustable autonomy; human may be wrongly biased
➢ Explanation of output
▪ Implementation requires organization level partnership:
➢ Immersion opens up our eyes to complexity; builds up trust over time
➢ Need a champion on the inside
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AI for Social Good
7/20/2017 32
THANK YOU
CAIS.USC.EDU
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