Post on 07-Jul-2020
AI for Social Good:
Key Techniques, Applications, and Results
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)
2/11/2017 2
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
Eradicate social isolation
End homelessness
…
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Overview of CAIS Project Areas
AI for Assisting Low Resource Communities
Social networks: Spread HIV information
Maximize influence under uncertainty
Real-world pilot tests: Big improvements
AI for Protecting Endangered Wildlife
Machine learning/planning: Anti poaching
Scale, boundedly rational poachers,…
Real-world: Uganda, South Asia…
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Gangs, Substance abuse,
Veterans mental health
Social networks, robust optimization,…
Behavioral models…
Real-world: Research in progress
AI for Public Safety and Security
Game theory: security optimization
Solve massive “security games”
Real-world: US Coast Guard, FAMS…
Overview of CAIS Project Areas
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Outline
Introduction
HIV Information among homeless youth
Wildlife Conservation
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AI Program: HEALER
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Outline: HIV Information & Homeless Youth
Domain of homeless youth and HIV information dissemination
Real World Challenges in Influence Maximization
POMDP Model and algorithms
Pilot Study
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Influence Maximization Background
Input:
Graph G
Influence Model I
Choose K nodes per time step
Number of time steps for influence spread T
Output:
K nodes per time step maximizing expected # influenced
nodes
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Independent Cascade Model
Propagation Probability (for each edge)
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Real World Challenges
Uncertain network state
Uncertainty in network structure
Adaptive selection
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Challenge 1: Uncertain Network State
B
C
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Challenge 2: Uncertain Network Structure
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Independent Cascade Model
Propagation Probability (for each edge)
Existence Probability (for uncertain edges only)
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HIV Prevention Programs: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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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
Outline: HIV Information & Homeless Youth
Domain of homeless youth and HIV information dissemination
Real World Challenges in Influence Maximization
POMDP Model and algorithms
Pilot Study
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POMDP Model
Sequential decision making under uncertainty
Homeless shelters – sequentially select nodes
Homeless shelters – network state not known
Action
Choose nodes
Observation: Which edges exist?
Adaptive Policy
REWARD
POMDP SOLVER
HIDDEN
World State: Actual
node/edge state
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Real world scale: Why is it hard to solve?
2300 states150C6 actions
Current offline and online
POMDP solvers unable to
scale
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Real world scale: Why is it hard to solve?
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Real world networks have community structure
Graph Partitioning
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Graph Partitioning
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HEALER : Hierarchical Ensembling
GRAPH PARTITION TOOL
. . . .
. . . .
GRAPH SAMPLER
ORIGINAL POMDP
Intermediate
POMDP
Sampled
POMDP
Sampled
POMDP
Sampled
POMDP
HEALER
Graph
SamplingGraph
Sampling
Graph
Sampling
Intermediate
POMDP
Intermediate
POMDP
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Graph Partitioning: Why partition?
qINTERMEDIATE
POMDP
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Real Networks - Solution Quality
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Robustness & Parameter Uncertainty
HEALER: fixed propagation and existence probability
Want policies robust to different possible values of P(A,B) and U(A,B)
Express as ranges of values, e.g., U(A,B) is in [0.4, 0.8]
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HEALER++ Robustness & Parameter Uncertainty
Worst case parameters: a zero-sum game against nature
Payoffs: (performance of algorithm)/OPT
Nature
Chooses parameters
P(A,B) and U(A,B)
vs
Algorithm
Chooses policy in
POMDP state-action
space
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Outline: HIV Information & Homeless Youth
Domain of homeless youth and HIV information dissemination
Real World Challenges in Influence Maximization
POMDP Model and algorithms
Pilot Study
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Pilot Tests with 170 Youth in LA Area
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
0
20
40
60
80
100
HEALER HEALER++ Degree
Percent of non-Peer Leaders
Informed Not Informed
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Results: Pilot Studies
0
20
40
60
80
100
HEALER HEALER++ Degree
Informed Non-Peer Leaders Who Started Testing for HIV
Testing Non-Testing
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AI Program: HEALER
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Next Steps
900 youth study begun at three locations in Los Angeles
300 enrolled in HEALER/HEALER++
300 enrolled in no condition
300 in Degree centrality
IRB approvals
Presenting video updates every few months
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Outline
Introduction
HIV Information among homeless youth
Wildlife Conservation
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Uganda Wildlife Authority & Wildlife Conservation Society
Protecting Wildlife in Uganda
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Predicting Poaching from Past Crime Data
PAWS: Applying AI for protecting wildlife
Poacher Behavior Prediction
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Queen Elizabeth National Park, Uganda
Poacher behavior prediction
1300 targets, 12 years of patrol data
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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Initial Attempt Using Dynamic Bayes Net:Time Dependency & Imperfect Observation
Attacking probability
Detection probability
Ranger observation
Ranger patrol
Animal density
Distance to rivers /
roads / villages
Area habitat
Area slope
…
M
j
jxSEU
jxSEU
j adversary
adversary
e
eq
1'
)',(
),(Adversary’s
probability of
choosing target j
Interpretability, Speed
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Poacher Behavior Prediction
Poacher behavior prediction
Classifier 1 Classifier 2 Classifier 3
0 1 1
Aggregation Rule
1
Majority
1
Ensemble of Decision Trees
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0
0.5
1
1.5
2
2.5
3
3.5
4
L&L
Uniform Random SVM CAPTURE Decision Tree BoostIT-5Experts-Maj
Poacher Behavior Prediction
Poacher attack prediction
Results from 2015
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Real-world Deployment: Results
Two 3 sq km patrol areas: Predicted hot spots with infrequent patrols
Trespassing: 19 signs of litter etc
Snaring: 1 active snare
Poached Animals: Poached elephant
Snaring: 1 elephant snare roll
Snaring: 10 Antelope snares
Hit rates (per month)
Ours outperforms 91% of months
Historical Base Hit
RateOur Hit Rate
Average: 0.73 3
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On-Going Experiments:Queen Elizabeth National Park
• Red: Group 1 (highest attack prediction rate)
• Yellow: Group 2
• Green: Group 3
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On-Going Experiments: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.
• Indirect poaching signs; pursuit of poachers
• Signs of road building, fires, illegal fishing
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Learn from crime data
Game Theory calculate
randomized patrols
Patrollers execute patrols
Poachers attack targets
Predicting Poaching from Past Crime Data
PAWS: Applying AI for protecting wildlife
Game Theory + Poacher Behavior Prediction
Towards the Future
Significant potential: AI for low resource communities, emerging markets
Direction of AI research in our hands
Not just applications; novel research challenges:
Fundamental computational challenges from use-inspired research
Designing AI systems in society:
• Interpretability
• Maintaining human autonomy
Methodological challenges:
Encourage interdisciplinary research: measures impact in society
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AI for Social Good
2/11/2017 48
THANK YOU
BACKUP
POMDP Model: Challenges
Number of states (node & edge uncertainty): ~ 2300
Number of actions (N-choose-K): > 1,000,000,000
Number of observations (Edges exist or not): Exponential
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Action
Choose nodes
Observation: Which edges exist?
Adaptive Policy
REWARD
POMDP SOLVER
HIDDEN
World State: Actual
node/edge state
Mission Statement: Advancing AI research driven by…
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Divide and Conquer
Main idea:
Divide POMDP into smaller POMDPs
Combine solutions of smaller POMDPs
Two different ways of dividing POMDPs
Using uncertain edges: PSINET
Using graph partitioning and sampling: HEALER
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…And the Past
“…prize every invention of
science made for the
benefit of all”
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Intermediate POMDP
Sampled
POMDP
Sampled
POMDP
Sampled
POMDP
.
.
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Intermediate POMDP
Sampled
POMDP
Sampled
POMDP
Sampled
POMDP
.
.
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Params #1 Params #2
Policy #1 0.8, -0.8 0.3, -0.3
Policy #2 0.7, -0.7 0.5, -0.5
HEALER++ Algorithm
Computes an equilibrium strategy for this game
Exponentially large strategy space: incremental generation, double oracle
Under some conditions, provably converges to approx. equilibrium
Params #1 Params #2
Policy #1 0.8, -0.8 0.3, -0.3
Policy #2 0.7, -0.7 0.5, -0.5
Nature’s oracle
Params #1 Params #2 Params #3
Policy #1 0.8, -0.8 0.3, -0.3 0.4, -0.4
Policy #2 0.7, -0.7 0.5, -0.5 0.6, -0.6
Influencer’s oracle
Params #1 Params #2 Params #3
Policy #1 0.8, -0.8 0.3, -0.3 0.4, -0.4
Policy #2 0.7, -0.7 0.5, -0.5 0.6, -0.6
Policy #3 0.6, -0.6 0.4, -0.4 0.7, -0.7
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Real networks - robustness
50
70
90
Venice Hollywood
Worst case % of optimal influence
HEALER++ HEALER
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