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AI for Social Good: Key Techniques, Applications, and Results

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)

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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 for Assisting 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 for Earth

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 for Public Safety and Security

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AI Program: HEALER

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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 = 51st time step

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Challenge: Adaptive selection in Uncertain Network

K = 52nd time step

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Challenge 3 : Adaptive selection

K = 53rd 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 forintervention

Observation: Which edges exist?

Adaptive Policy

Algorithm to FINDADAPTIVE POLICY

Actual socialNetwork in the

real world:

Real Networks – Simulation Results [2016-2017]

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0

10

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Venice Hollywood

Indirect Influen

ce

Degree CR PSINET HEALER‐1 HEALER‐2

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Pilot Testswith 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

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40

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HEALER HEALER++ Degree

Percent of non-Peer Leaders

Informed Not Informed

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20

40

60

80

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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

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“Picking youth as peer leaders waschanging their self esteem and thesense of confidence that they couldbe an agent for positive change….”

Eric Rice

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Overview of CAIS Project Areas

AI for Assisting Low Resource Communities

Substance abuse, suicide prevention…

Modeling gang violence, matching homeless and homes…

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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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Data from Queen Elizabeth National Park, Uganda

Poacher behavior prediction [2016]

Number of poaching attacks over 12 years: ~1000

How likely is an attack on

a grid Square

Ranger patrolfrequency

Animal density

Distance to rivers / roads

Area habitat

Area slope

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0.5

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1.5

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2.5

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3.5

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L&L

Uniform Random SVM CAPTURE Decision Tree Our Best Model

Poacher Behavior Prediction

Poacher Attack Prediction [2017]

Results from 2015

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Real-world Deployment: (1 month)

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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 Rate

Our 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)

Catch per Unit Effort

Experiment Group

Real-world Deployment: Field Test 2 (6 months) [2017]

Catch Per Unit Effort (CPUE)

Unit Effort = km walkedHistorical CPUE: 0.04Our high CPUE: 0.11

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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

AI for Social Good

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THANK YOU

CAIS.USC.EDU

THANK YOU

CAIS.USC.EDU

Towards the Future: AI for Social Good

Significant potential: AI for low resource communities, emerging markets

Not just applications; novel research challenges: Fundamental computational challenges from use-inspired research Designing AI systems in society:

• Interpretability• Complementing human autonomy

Methodological challenges: Encourage interdisciplinary research: measures impact in society

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