Applying AI in preventative health interventions ...Applying AI in preventative health...

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Applying AI in preventative health interventions: Algorithms, deployments and fairness

MILIND TAMBE

Director Center for Research on Computation and Society

Harvard University

Director “AI for Social Good”

Google Research India

Date: 12/14/2019 2

AI and Multiagent Systems Research for Social Impact

Public Safety

and Security

ConservationPublic Health

Viewing Social Problems as Multiagent Systems

Key research challenge across problem areas:

Optimize Our Limited Intervention Resources

Interacting with Other Agents

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Multiagent Systems Reasoning:

Social Networks, Game Theory

Reasoning with Social Networks

▪ Social networks to enhance intervention, e.g., HIV information

▪ Real-world pilot tests: Homeless youth shelters in Los Angeles

Public HealthOptimizing Limited Intervention (Health Worker) Resources

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▪ Security games and adversary (poacher) behavior prediction

▪ Real-world: National parks in Uganda, Malaysia…

Green Security Games

Conservation/Wildlife ProtectionOptimizing Limited Intervention (Ranger) Resources

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▪ Game Theory for security resource optimization

▪ Real-world: US Coast Guard, US Federal Air Marshals Service…

Public Safety and SecurityOptimizing Limited Intervention (Security) Resources

Stackelberg Security Games

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Common ThemesInterdisciplinary Partnerships, Multiagent Systems, Data-to-deployment pipeline

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Field tests&

deployment

Prescriptivealgorithm

Multiagent Reasoning; Intervention

Immersion

Data Collection

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Predictivemodel

Learning/Expert input

Common ThemesInterdisciplinary Partnerships, Multiagent Systems, Data-to-deployment pipeline

Fairness Challenges in AI for Public Health

Field tests

Prescribe

Select key influencers

PartialSocial

network

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Predict

Link Prediction

Fairness challenges in intervention; not just prediction

Field tests

Prescribe

PartialSocial

network

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Predict

Link Prediction

Field tests

Prescribe

PartialSocial

network

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Predict

Link Prediction

Address fairness: Need collaboration of AI & public health stakeholders

Fairness & AI for Public Health

HIV prevention

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TB Prevention

Suicide Prevention

AI Interventions in Public health

Fairness challenges in intervention

PhD student photos in top right corner

Public HealthOptimizing Limited Intervention (Social Worker) Resources

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Preventing HIV in homeless youth: Rates of HIV 10 times housed population

➢ Shelters: Limited number of peer leaders to spread HIV information in social networks

➢ “Real” social networks gathered from observations in the field; not facebook etc

Influence Maximization Background

▪ Given:

▪ Social network Graph G

▪ Choose K “peer leader” nodes

▪ Objective:

▪ Maximize expected number of influenced nodes

▪ Assumption: Independent cascade model of information spread

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Independent Cascade Model and Real-world Physical Social Networks

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A BP(A,B)=0.4

0 1μ = 0.5

0 1μ ∈ [0.3, 0.7]

Robust, Dynamic Influence Maximization

▪ Worst case parameters: a zero-sum game against nature

▪ Payoffs: (performance of algorithm)/OPT

Nature

Chooses parameters

μ,σvs

Algorithm

Chooses policy, i.e.,

Chooses Peer leaders

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Wilder

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 [2017]Robust, Dynamic Influence Maximization

▪ Equilibrium strategy despite exponential strategy spaces: Double oracle

Influencer’s oracle

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

Policy #3 0.6, -0.6 0.4, -0.4 0.7, -0.7

Nature

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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

Wilder

Theorem: Converge with approximation guarantees

\ Params #1 Params #2

Policy #1 0.8, -0.8 0.3, -0.3

Policy #2 0.7, -0.7 0.5, -0.5

Policy #3 0.6, -0.6 0.4, -0.4

Challenge: Multi-step Policy

1st time step

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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

2nd time step

WilderYadav

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HEALER: POMDP Model for Multi-Step PolicyRobust, Dynamic Influence Maximization

Action

Choose nodes

Observation: Update

propagation probability

Policy

HIDDEN STATE

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

partitions

Pilot Tests with HEALER

with 170 Homeless Youth [2017]

Recruited youths:

HEALER HEALER++ DEGREE CENTRALITY

62 56 55

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12 peer leaders

Yadav Wilder

Results: Pilot Studies [2017]

HEALER HEALER++ Degree

Percent of non-Peer Leaders

Informed Not Informed

HEALER HEALER++ Degree

Informed Non-Peer Leaders Who Started Testing for HIV

Testing Non-Testing

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Yadav Wilder

More details: Journal of Society of Social Work & Research (Nov 2018)

Data to Deployment Pipeline:Network Sampling to avoid Data Collection Bottleneck

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Data collection costly Sample 18%

Sampling from largest

communities

Wilder

New experiment With 60 homeless youth

12 peer leaders

Results: Pilot Studies

with New Sampling Algorithm [2018]

SAMPLE HEALER Degree

Percent of non-Peer Leaders

Informed Not Informed

SAMPLE HEALER Degree

Informed Non-Peer Leaders Who Started Testing for HIV

Testing Non-Testing

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Wilder

AI Assistant: HEALER

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Continuing Research on HIV prevention [2019]

▪ Completing 900 youth study at three homeless shelters

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Public Health: Optimizing Limited Social Worker ResourcesPreventing Tuberculosis in India [2019]

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Tuberculosis (TB): ~500,000 deaths/year, ~3M infected in India

➢ Patient in low resource communities: Non-adherence to TB Treatment

➢ Digital adherence tracking: Patients call phone #s on pill packs; many countries

➢ Predict adherence risk from phone call patterns? Intervene before patients miss dose

TB Treatment Adherence but Limited Resources:

Intervening Selectively before patients miss doses

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Data Collect

Phone logs

Predicthigh risk patients

RF or LSTM

Prescription

ConstraintTop K

Killian

➢ 15K patients, 1.5M calls

Increasing TB Treatment Adherence:

Intervening before patients miss doses [2019]

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Killian

Data from

State ofMaharashtra

True Positives False Positives

Best Model vs. Baseline: Prediction High Risk Patients

Baseline Best Model

+35% -19%

Improving TB interventions

Decision-Focused Methods (simultation study)

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Wilder

Interventions: Decision-Focused Better

Stage by Stage Decision Focused

Decision focused learning improves TB interventions

Integrating with Everwell’s Platform

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Killian

This work has a lot of potential to save lives.

Bill ThiesCo-founder, Everwell Health Solutions

Suicide Prevention in Marginalized Populations: Choose Gatekeepers in social networks

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Rahmattalabi

Suicide rate is significantly high among youth experiencing homelessness (~7%-26%)

More than 50% have either had thoughts of suicide or have made an attempt

Suicide Prevention in Marginalized Populations: Choose Gatekeepers in social networks

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▪ Worst case availability: constant-sum game against nature

Rahmattalabi

Nature

Chooses some

gatekeepers to not

participate

Algorithm

Chooses K gatekeepers

Train: Available: Covered:

Fairness & AI for Public Health

HIV prevention

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TB Prevention

Suicide Prevention

AI and Public health

Fairness challenges in intervention

Suicide Prevention in Marginalized Populations: Choose Gatekeepers in social networks (NeurIPS 2019)

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Rahmattalabi

Is there any Inequality in coverage of marginalized populations?

Suicide Prevention in Marginalized Populations: Choose Gatekeepers in social networks (NeurIPS 2019)

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Rahmattalabi

▪ Inequality in coverage of marginalized population

Maximin Fairness:Towards addressing Fairness Challenges

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Rahmattalabi▪ Maximin fairness constraints: Every group at least W coverage

HIV Prevention in Marginalized Populations: Choose Peer Leaders (IJCAI 2019)

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• Intervention: Influence maximization

• Are there any fairness violations?

NativeAmerican

Asian Black NativeHawaiian

White Latino

Fairness violation

Greedy

HIV Prevention in Marginalized Populations: Choose Peer Leaders (IJCAI 2019)

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• Are there any fairness violations?

Budget: k = 5

Optimal value with 𝑘 = 3: 4.25

Constraint:f 𝑆 ≥ 4.25

𝑝 = 0.25

Optimal value with 𝑘 = 2: 2.5

Constraint:f 𝑆 ≥ 2.5

k = 3 k = 2

15⋅ 5

Multiobjective Influence MaximizationTowards Addressing Fairness Challenges

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NativeAmerican

Asian Black NativeHawaiian

White Latino

Fairness violation

Greedy FairInfluence

• Four networks from HIV prevention interventions:~70 nodes each

• Compare standard greedy algorithm to both fairness concepts

• Is more attention or less attention fair?

• What are fairness concerns in south Asian context?

• Is interpretability a requirement for fairness?

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Fairness in TB Treatment

AI & Multiagent Systems for Social Impact

Fairness and AI for Public HealthChallenges in Intervention

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Fairness challenges in intervention; important to step beyond prediction

No consensus on exact fairness approach; rising concerns AI & fairness

Public health officials/stakeholders involved from start in fair solutions

Collaboration between AI systems & Stakeholders to address fairness

Thank you!

42Date: 12/14/2019

Applying AI in preventative health interventions ...Applying AI in preventative health...

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