AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. In pursuit of this goal of AI for Social Impact, we as AI researchers must go beyond improvements in computational methodology; it is important to step out in the field to demonstrate social impact. To this end, we focus on the problems of public safety and security, wildlife conservation, and public health in low-resource communities, and present research advances in multiagent systems to address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. We present case studies from our deployments around the world as well as lessons learned that we hope are of use to researchers who are interested in AI for Social Impact. In pushing this research agenda, we believe AI can indeed play an important role in fighting social injustice and improving society.

[1]  Manish Jain,et al.  Software Assistants for Randomized Patrol Planning for the LAX Airport Police and the Federal Air Marshal Service , 2010, Interfaces.

[2]  Leandro Soriano Marcolino,et al.  Preventing HIV Spread in Homeless Populations Using PSINET , 2015, AAAI.

[3]  Bo An,et al.  Deploying PAWS: Field Optimization of the Protection Assistant for Wildlife Security , 2016, AAAI.

[4]  Eric Rice,et al.  Group-Fairness in Influence Maximization , 2019, IJCAI.

[5]  Madhukar Pai,et al.  Digital adherence technologies for the management of tuberculosis therapy: mapping the landscape and research priorities , 2018, BMJ Global Health.

[6]  Nicole Immorlica,et al.  Uncharted but not Uninfluenced: Influence Maximization with an Uncertain Network , 2017, AAMAS.

[7]  Neha Kumar,et al.  Empowerment on the Margins: The Online Experiences of Community Health Workers , 2019, CHI.

[8]  Milind Tambe,et al.  One Size Does Not Fit All: A Game-Theoretic Approach for Dynamically and Effectively Screening for Threats , 2016, AAAI.

[9]  Milind Tambe,et al.  Piloting the Use of Artificial Intelligence to Enhance HIV Prevention Interventions for Youth Experiencing Homelessness , 2018, Journal of the Society for Social Work and Research.

[10]  Masahiro Kimura,et al.  Tractable Models for Information Diffusion in Social Networks , 2006, PKDD.

[11]  Milind Tambe,et al.  End-to-End Influence Maximization in the Field , 2018, AAMAS.

[12]  Milind Tambe,et al.  Adversary Models Account for Imperfect Crime Data: Forecasting and Planning against Real-world Poachers , 2018, AAMAS.

[13]  Mina Guirguis,et al.  Don't Bury your Head in Warnings: A Game-Theoretic Approach for Intelligent Allocation of Cyber-security Alerts , 2017, IJCAI.

[14]  Sarit Kraus,et al.  Deployed ARMOR protection: the application of a game theoretic model for security at the Los Angeles International Airport , 2008, AAMAS 2008.

[15]  Milind Tambe,et al.  Security and Game Theory: Evaluating Deployed Decision-Support Systems for Security: Challenges, Analysis, and Approaches , 2011 .

[16]  Milind Tambe,et al.  Cloudy with a Chance of Poaching: Adversary Behavior Modeling and Forecasting with Real-World Poaching Data , 2017, AAMAS.

[17]  Sarit Kraus,et al.  Game-Theoretic Patrolling with Dynamic Execution Uncertainty and a Case Study on a Real Transit System , 2014, J. Artif. Intell. Res..

[18]  Eric Rice,et al.  Exploring Algorithmic Fairness in Robust Graph Covering Problems , 2020, NeurIPS.

[19]  Haifeng Xu,et al.  Optimal Patrol Planning for Green Security Games with Black-Box Attackers , 2017, GameSec.

[20]  Sarit Kraus,et al.  Using Game Theory for Los Angeles Airport Security , 2009, AI Mag..

[21]  Angela A. Aidala,et al.  Why Housing? , 2007, AIDS and Behavior.

[22]  Milind Tambe,et al.  End-to-End Game-Focused Learning of Adversary Behavior in Security Games , 2020, AAAI.

[23]  Bo An,et al.  Stackelberg Security Games: Looking Beyond a Decade of Success , 2018, IJCAI.

[24]  Bo An,et al.  PROTECT: a deployed game theoretic system to protect the ports of the United States , 2012, AAMAS.

[25]  R. Winett,et al.  Randomised, controlled, community-level HIV-prevention intervention for sexual-risk behaviour among homosexual men in US cities , 1997, The Lancet.

[26]  Milind Tambe,et al.  Optimizing Network Structure for Preventative Health , 2018, AAMAS.

[27]  Haifeng Xu,et al.  The Mysteries of Security Games: Equilibrium Computation Becomes Combinatorial Algorithm Design , 2016, EC.

[28]  M. Fraser,et al.  Steps in Intervention Research: Designing and Developing Social Programs , 2010 .

[29]  B. Stengel,et al.  Leadership with commitment to mixed strategies , 2004 .

[30]  Milind Tambe,et al.  Robust Peer-Monitoring on Graphs with an Application to Suicide Prevention in Social Networks , 2019, AAMAS.

[31]  Milind Tambe,et al.  Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data , 2019, KDD.

[32]  Ramakant Nevatia,et al.  SPOT Poachers in Action: Augmenting Conservation Drones With Automatic Detection in Near Real Time , 2018, AAAI.

[33]  Manish Jain,et al.  Security Games with Arbitrary Schedules: A Branch and Price Approach , 2010, AAAI.

[34]  Milind Tambe,et al.  Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization , 2018, AAAI.