All of the papers should be available through google scholar. Logging into Harvard library should also make it easy to access these papers.
Definitions, course structure:
1. (Jan 26) Lecture 1: Course intro, syllabus, goals, key concepts: Perspectives on AI in Society; overview.
Lecture 1 slides
Handout: Syllabus
Optional readings:
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L. Floridi, J.Cowles, T. King, M. Taddeo “How to design AI for Social Good: Seven Essential Factors” Science and Engineering Ethics (2020) 26:1771–179
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Reference: Z. R. Shi, C. Wang, F. Fang “AI for Social Good: A survey”, arXiv, 2020
AI for social good case studies, philosophy:
2. (Jan 28) Lecture 2: AI for social impact case studies and discussion
Lecture video discussing AI and public safety
Lecture 2 slides
3. (Feb 2) Lecture 3: AI for social impact case studies and discussion
Lecture video discussing AI for public health and conservation
Lecture 3 Slides
The goal of these two lectures is to explain my own group’s previous projects over the past 15 years in AI for Social Impact. I will discuss key projects, how they came about, what was the motivation, their progress, obstacles, lessons learned at the end.
Optional readings:
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Andrew Perrault, Fei Fang, Arunesh Sinha, and Milind Tambe. 2020. “AI for Social Impact: Learning and Planning in the Data-to-Deployment Pipeline.” AI Magazine.
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Bryan Wilder, Laura Onasch-Vera, Graham Diguiseppi, Robin Petering, Chyna Hill, Amulya Yadav, Eric Rice, and Milind Tambe. 2021. “Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness.” In AAAI Conference on Artificial Intelligence.
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L. Xu, S. Gholami, S. Mc Carthy, B. Dilkina, A. Plumptre, M. Tambe, R. Singh, M. Nsubuga, J. Mabonga, M. Driciru, F. Wanyama, A. Rwetsiba, T. Okello, E. Enyel Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations In IEEE International Conference on Data Engineering (ICDE) , March 2020
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Detlof von Winterfeldt R. Scott Farrow Richard S. John Jonathan Eyer Adam Z. Rose Heather Rosoff “Assessing the Benefits and Costs of Homeland Security Research: A Risk‐Informed Methodology with Applications for the U.S. Coast Guard” First published: 15 October 2019
Background readings:
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D. Kempe, J. Kleinberg, and É. Tardos. Maximizing the Spread of Influence through a Social Network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 137–146. ACM, 2003.
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H. Kamarthi, P. Vijayan, B. Wilder, B. Ravindran,M. Tambe Influence maximization in unknown social networks: Learning Policies for Effective Graph Sampling In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May, 2020
4. (Feb 4) Lecture 4: AI for Substance Abuse prevention (Invited lecture by Prof. Amulya Yadav)
Required readings
- Maryam Tabar, Heesoo Park, Stephanie Winkler, Dongwon Lee, Anamika Barman-Adhikari, Amulya Yadav “Identifying Homeless Youth At-Risk of Substance Use Disorder: Data-Driven Insights for Policymakers” KDD 2020
- Amulya Yadav, Roopali Singh, Nikolas Siapoutis, Anamika Barman-Adhikari, Yu Liang “Optimal and Non-Discriminative Rehabilitation Program Design for Opioid Addiction Among Homeless Youth” IJCAI 2020
- Zi-Yi Dou, Anamika Barman-Adhikari, Fei Fang, Amulya Yadav “Harnessing Social Media to Identify Homeless Youth At-Risk of Substance Use”, AAAI 2021
Optional Readings:
- Rice, E., Milburn, N. G., & Monro, W. (2011). Social networking technology, social network composition, and reductions in substance use among homeless adolescents. Prevention Science, 12(1), 80-88.
- Thomas W Valente, Beth R Hoffman, Annamara Ritt-Olson, Kara Lichtman, and C Anderson Johnson. 2003. Effects of a social-network method for group assignment strategies on peer-led tobacco prevention programs in schools. American journal of public health 93, 11 (2003), 1837–1843.
Ethics of AI for Social Good
5. (Feb 9) Lecture 5: AI & Ethics (Embedded ethics)
LECTURE 5 SLIDES
Ethical reasoning is an essential skill for today’s computer scientists. The Embedded EthiCS distributed pedagogy embeds philosophers directly into computer science courses to teach students how to think through the ethical and social implications of their work.
- Thomas W Valente, Beth R Hoffman, Annamara Ritt-Olson, Kara Lichtman, and C Anderson Johnson. 2003. Effects of a social-network method for group assignment strategies on peer-led tobacco prevention programs in schools. American journal of public health 93, 11 (2003), 1837–1843.
- A. Rahmattalabi, A. Yadav, B. Wilder, A. Fulginiti, P. Vayanos, E. Rice, M. Tambe Exploring Algorithmic Fairness in Robust Graph Covering Problems In Proceedings Conference on Neural Information Processing Systems (NeurIPS), December, 2019
Defining projects
6. (Feb 11) Lecture 6: Prof. Chris Golden: Public health and conservation
- Sarah Whitmee, Andy Haines, Chris Beyrer, Frederick Boltz, Anthony G Capon, Braulio Ferreira de Souza Dias, Alex Ezeh, Howard Frumkin, Peng Gong, Peter Head, Richard Horton, Georgina M Mace, Robert Marten, Samuel S Myers, Sania Nishtar, Steven A Osofsky, Subhrendu K Pattanayak, Montira J Pongsiri, Cristina Romanelli, Agnes Soucat, Jeanette Vega, Derek Yach “Safeguarding human health in the Anthropocene epoch:report of The Rockefeller Foundation–Lancet Commission on planetary health” The Rockefeller Foundation–Lancet Commission on planetary health
7. (Feb 16) Lecture 7: WWF Invited lecture (Rohit Singh)
WWF LECTURE SLIDES
Background on Tuesday's WWF lecturer, Rohit Singh.
https://tigers.panda.org/reports/ (Links to an external site.)
https://wwf.panda.org/discover/our_focus/wildlife_practice/wildlife_trade/wildlife_crime_initiative/
8. (Feb 18) Lecture 8: AI and COVID-19: debate. Role of in the COVID-19 fight?
Pointer to Michael Mina lecture at CRCS
Prof. Michael Mina (invited lecture)
- B. Wilder, M. Charpignon, J. Killian, H. Ou, A. Mate, S. Jabbari, A. Perrault, A. Desai, M. Tambe, M. Majumder “Modeling between-population variation in COVID-19 dynamics in Hubei, Lombardy, and New York City” In Proceedings of the National Academy of Sciences (PNAS), 117(41): 25904-25910, 2020.
- D. Larremore, B. Wilder, E. Lester, S. Shehata, J. Burke, J. Hay, M. Tambe, M. Mina, R. Parke “Test sensitivity is secondary to frequency and turnaround time for COVID-19 surveillance” In Science Advances, eabd5393, 2020
9. (Feb 23) Lecture 9: Discussion AI for Social Good; plus bandit models for health intervention
Discussion (available on most streaming services):
POVERTY INC
Papers:
- A. Mate*, J. Killian*, H. Xu, A. Perrault, M. Tambe (* equal contribution) Collapsing Bandits and Their Application to Public Health Interventions In Proceedings Conference on Neural Information Processing Systems (NeurIPS), December, 2020
- A. Biswas, G. Aggarwal, P. Varakantham, M. Tambe Learning Index Policies for Restless Bandits with Application to Maternal Healthcare In International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2021
Optional:
- P. Whittle. Restless bandits: Activity allocation in a changing world. J. Appl. Probab., 25(A): 287–298, 1988.
- R. R. Weber and G. Weiss. On an index policy for restless bandits. J. Appl. Probab., 27(3): 637–648, 1990.
10. (Feb 25) Lecture 10: Implementation Science by Prof. Shoba Ramanadhan
Shoba Ramanadhan Lecture Slides on implementation science
- Mark S Bauer, JoAnn Kirchner "Implementation science: What is it and why should I care" Psychiatry Research
https://pubmed.ncbi.nlm.nih.gov/31036287/
- Mark S. Bauer, Laura Damschroder, Hildi Hagedorn, Jeffrey Smith & Amy M. Kilbourne “An introduction to implementation science for the non-specialist” BMC Psychology volume 3, Article number: 32 (2015)
STUDENT PAPER PRESENTATIONS
11. (Mar 2) Lecture 11: Student paper presentation
Paper: Project RISE: Recognizing Industrial Smoke Emissions
Appears at: AAAI 2021 (to appear), Special Track on AI for Social Impact
RainBench: Towards Global Precipitation Forecasting from Satellite Imagery
Appears at:
Paper: A Markov Decision Process Model for Socio-Economic Systems Impacted by Climate Change
Appears at: ICML 2020
Paper: Security and privacy in Smart Farming: Challenges and Opportunities
Appears at: IEEE Access 8 (2020)
12. (Mar 4) Lecture 12: Student presentations
Paper: Dual-Mandate Patrols: Multi-Armed Bandits for Green Security
Appears at: AAAI-21
Paper: Protecting Geolocation Privacy of Photo Collections
Appears at: AAAI-2020
Paper: Subverting Privacy-Preserving GANs: Hiding Secrets in Sanitized Images
Appears at: AAAI-21 - Special Track on AI for Social Impact
Paper: Weakly-Supervised Fine-Grained Event Recognition on Social Media Texts for Disaster Management
Appears at: AAAI 2020 Special Technical Track: AI for Social Impact
13. (Mar 9) Lecture 13: Student presentations
Paper: Predicting Patient Outcomes with Graph Representation Learning
Appears at: AAAI 2021
Paper: Personalizing ASR for Dysarthric and Accented Speech with Limited Data
Appears at: Interspeech 2019
Appears at: AAAI 2021 Special Track on AI for Social Impact
14. (Mar 11) Lecture 14: Midterm short presentations on project progress
March 16: Wellness day no courses meet
AI and sustainability: Learning to appreciate an interdisciplinary perspective
15. (Mar 18) Lecture 15:Invited Lecture on AI and Sustainability by Prof. Andrew Davies, Harvard EOB
Andrew Davies Lecture Slides
Brodrick PG, Davies AB, and Asner GP. 8/2019. “Uncovering ecological patterns with convolutional neural networks.Links to an external site.” Trends in Ecology and Evolution, 34, 8, Pp. 734-745. Publisher's Version
From Data to Deployment in AI4SG
16. (Mar 23) Lecture 16: Invited lecture by Bryan Wilder on measuring impact
BRYAN WILDER SLIDES ON MEASURING IMPACT
17. (Mar 25) Lecture 17: Discuss broader impacts of class projects
M. Latonero “Opinion: AI For Good Is Often Bad” Wired November 2019
The conversation “AI algorithms intended to root out welfare fraud often end up punishing the poor instead”, Rawstory Feb 14, 2020
Beyond ‘AI for Social Good’ (AI4SG): social transformations—not tech-fixes—for health equity
Cheryl Holzmeyer, Institute for Social Transformation, University of California, Santa Cruz, CA, USA
E. Gibney “The battle for ethical AI at the world’s biggest machine-learning conference” Nature news, January 2020
18. (Mar 30) Lecture 18: Invited Lecture by Phil Nelson, Google
Fairness, Accountability, Transparency
19. (Apr 1) Lectures 19: Fairness in AI for Social Good
FAIRNESS & AI FOR SOCIAL GOOD
A. Rahmattalabi, A. Yadav, B. Wilder, A. Fulginiti, P. Vayanos, E. Rice, M. Tambe Exploring Algorithmic Fairness in Robust Graph Covering Problems In Proceedings Conference on Neural Information Processing Systems (NeurIPS), December, 2019
A. Rahmattalabi, S. Jabbari, P. Vayanos, H. Lakkaraju, M. Tambe Fair Influence Maximization: a Welfare Optimization Approach In In AAAI conference on Artificial Intelligence (AAAI), February, 2021
20. (Apr 6) Lecture 20: Question-answer session on second round of paper readings
Paper: GLTR: Statistical Detection and Visualization of Generated Text
Appears at: ACL 2019 Demo Track
Paper: A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning
Appeared at: AAAI 2020
Paper: Predicting Forest Fire Using Remote Sensing Data And Machine Learning
Appeared at: AAAI 2021, Special Track on AI for Social Impact
Paper: Combining satellite imagery and machine learning to predict poverty
Appears at: Science, Vol. 353, Issue 6301, 2016
Paper: Inferring Nighttime Satellite Imagery from Human Mobility
Appears at: AAAI 2020 Special Track on AI for Social Impact
Appears at: Interspeech 2019
Paper: Minimizing Energy Use of Mixed-Fleet Public Transit for Fixed-Route Service
Appears at: AAAI 2021
Paper: Spatio-Temporal Attention-Based Neural Network for Credit Card Fraud Detection
Appears at: AAAI-2021, Special Track on AI for Social Impact
Paper: Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set
Appears at: PNAS 2020
Paper: How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19?
Appears at: NeurIPS 2020
21. (Apr 8) Lecture 21: AI for Social Good and HCI
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Enabling Data-Driven API Design with Community Usage Data: A Need-Finding Study
Tianyi Zhang, Björn Hartmann, Miryung Kim, and Elena Glassman. CHI 2020. -
Community power could boost confidence in vaccination programmes
RC Wurth, H Saksono
Nature 589 (7841), 198 -
Storywell: Designing for Family Fitness App Motivation by Using Social Rewards and Reflection
Herman Saksono, Carmen Castaneda-Sceppa, Jessica Hoffman, Vivien Morris, Magy Seif El-Nasr, Andrea Parker. 2020. Storywell: Designing for Family Fitness App Motivation by Using Social Rewards and Reflection. In CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2020), May 4–9, 2019, Honolulu, HI, USA. ACM, New York, NY, USA, 13 pages. (PDF)3. -
Social technologies for digital wellbeing among marginalized communities
MA Devito, AM Walker, J Birnholtz, K Ringland, K Macapagal, A Kraus, ...
Conference Companion Publication of the 2019 on Computer Supported
22. (Apr 13) Lecture 22: Interpretability in machine learning Invited Lecture by Dr. Hima Lakkaraju
Hima Lakkaraju Lecture Slides on Explainability in ML