# Lectures ### AI4SI book is available at [ai4sibook.org](https://ai4sibook.org) SortDateTopicLecturerAssignment1/22 Class Introduction Milind Tambe 1/24 Overview of AI for Social Impact Projects Milind Tambe Lily Xu\*, Shahrzad Gholami \*, Sara Mc Carthy, Bistra Dilkina, Andrew Plumptre, Milind Tambe, Rohit Singh, Mustapha Nsubuga, Joshua Mabonga, Margaret Driciru, Fred Wanyama, Aggrey Rwetsiba, Tom Okello, and Eric Enyel. 4/20/2020. “[Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations](https://teamcore.seas.harvard.edu/publications/stay-ahead-poachers).” In IEEE International Conference on Data Engineering (ICDE-20).[Abstract](https://teamcore.seas.harvard.edu/publications/stay-ahead-poachers) [icde\_arxiv\_version.pdf](https://teamcore.seas.harvard.edu/sites/projects.iq.harvard.edu/files/teamcore/files/icde_arxiv_version.pdf) Lily Xu, Andrew Perrault, Fei Fang, Haipeng Chen, and Milind Tambe. 7/27/2021. “[Robust Reinforcement Learning Under Minimax Regret for Green Security](https://teamcore.seas.harvard.edu/publications/robust-reinforcement-learning-under-minimax-regret-green-security).” Conference on Uncertainty in Artificial Intelligence (UAI).[Abstract](https://teamcore.seas.harvard.edu/publications/robust-reinforcement-learning-under-minimax-regret-green-security) [xu\_uai21\_robust\_rl.pdf](https://teamcore.seas.harvard.edu/sites/projects.iq.harvard.edu/files/teamcore/files/xu_uai21_robust_rl.pdf) 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](https://teamcore.seas.harvard.edu/publications/clinical-trial-ai-augmented-intervention-hiv-prevention-youth-experiencing).” In AAAI Conference on Artificial Intelligence.[Abstract](https://teamcore.seas.harvard.edu/publications/clinical-trial-ai-augmented-intervention-hiv-prevention-youth-experiencing) [aaai21\_hiv.pdf](https://teamcore.seas.harvard.edu/sites/projects.iq.harvard.edu/files/teamcore/files/aaai21_hiv.pdf) Kai Wang\*, Shresth Verma\*, Aditya Mate, Sanket Shah, Aparna Taneja, Neha Madhiwalla, Aparna Hegde, and Milind Tambe. 2/14/2023. “[Scalable Decision-Focused Learning in Restless Multi-Armed Bandits with Application to Maternal and Child Health](https://teamcore.seas.harvard.edu/publications/scalable-decision-focused-learning-restless-multi-armed-bandits-application).” AAAI Conference on Artificial Intelligence (AAAI).[Abstract](https://teamcore.seas.harvard.edu/publications/scalable-decision-focused-learning-restless-multi-armed-bandits-application) [10767.wangk\_full.pdf](https://teamcore.seas.harvard.edu/sites/projects.iq.harvard.edu/files/teamcore/files/10767.wangk_full.pdf) Shresth Verma, Aditya Mate, Kai Wang, Neha Madhiwala, Aparna Hegde, Aparna Taneja, and Milind Tambe. 5/28/2023. “[Restless Multi-Armed Bandits for Maternal and Child Health:Results from Decision-Focused Learning](https://teamcore.seas.harvard.edu/publications/restless-multi-armed-bandits-maternal-and-child-healthresults-decision-focused-0).” In International Conference on Autonomous Agents and Multiagent Systems (AAMAS). [dfl\_2022\_study\_aamas\_2023\_camera\_ready.pdf](https://teamcore.seas.harvard.edu/sites/projects.iq.harvard.edu/files/teamcore/files/dfl_2022_study_aamas_2023_camera_ready.pdf) Arshika Lalan, Paula Rodriguez Diaz, Panayiotis Danassis, Amrita Mahale, Kumar Madhu Sudan, Aparna Hegde, Milind Tambe, and Aparna Taneja. 2/28/2024. “[Improving Health Information Access in the World’s Largest Maternal MobileHealth Program via Bandit Algorithms](https://teamcore.seas.harvard.edu/publications/improving-health-information-access-world%E2%80%99s-largest-maternal-mobilehealth).” In Innovative Applications of Artificial Intelligence (IAAI). [iaai\_2024\_kilkari\_camera\_ready\_1.pdf](https://teamcore.seas.harvard.edu/sites/projects.iq.harvard.edu/files/teamcore/files/iaai_2024_kilkari_camera_ready_1.pdf) 1/29 AI and Public Health Jessica Haberer [Benitez et al.](/file_url/137) [Navarro et al.](/file_url/136) [David Adam](/file_url/138) 1/31 AI and Conservation Andrew Davies [Uncovering Ecological Patterns with Convolutional Neural Networks](/file_url/134) [Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino Habitats](/file_url/135) 2/5 AI and Bias & Fairness Sharad Goel [Risk Scores, Label Bias, and Everything but the Kitchen Sink](/file_url/139) 2/7 AI for Social Impact Discussion on Partnerships Milind Tambe [Lessons from ICT and International Development](/file_url/140) 2/12 Public Health Challenges in Madagascar Dimeji Mudele & Marissa Lynn Childs (Chris Golden lab) [A three-step machine learning approach for algal bloom detection using stationary RGB camera images](/file_url/141) [Improving accuracy of air pollution exposure measurements](/file_url/142) 2/14 LLMs, Generative AI, and Social Good Marinka Zitnik [Zero-shot drug repurposing with geometric deep learning and clinician centered design](/file_url/144) [Deep learning for diagnosing patients with rare genetic diseases](/file_url/143) 2/19 Presidents' Day - No Class 2/21 In-class OH for projects Project Proposal Due 2/26 Initial Survey Results I 2/28 Initial Survey Results II 3/4 Beyond the AI hype: Balancing innovation and Social Responsibility Virginia Dignum 3/6 Midterm Project Presentations Midterm Project Presentation Due 3/11 Spring Break - No Class 3/13 Spring Break - No Class 3/18 Harvard Business School Case Study: PAWS Brian Trelstad 3/20 Causal Inference for Conservation Lily Xu 3/25 Work period 3/27 Partnerships for AI4SI 4/1 Paper Discussion 4/3 Paper Discussion 4/8 Guest Lecture Malihe Alikhani Final Survey Paper Due. 4/10 Ethics Lecture Embedded Ethics 4/15 Implementation Science Shoba Ramnadhan 4/17 Work Period 4/22 Final Presentations 4/24 Final Presentations --- Attachments- [ picture\_as\_pdf brodrick\_etal\_2019\_tree\_using\_cnns\_in\_ecology.pdf ](/sites/g/files/omnuum6166/files/cs288/files/brodrick_etal_2019_tree_using_cnns_in_ecology.pdf) - [ picture\_as\_pdf biasinairesearch.pdf ](/sites/g/files/omnuum6166/files/cs288/files/biasinairesearch.pdf) - [ picture\_as\_pdf benitez\_sl\_uarto.pdf ](/sites/g/files/omnuum6166/files/cs288/files/benitez_sl_uarto.pdf) - [ picture\_as\_pdf medicalai\_dangerous\_poorer\_nations\_who.pdf ](/sites/g/files/omnuum6166/files/cs288/files/medicalai_dangerous_poorer_nations_who.pdf) - [ picture\_as\_pdf 2305.12638.pdf ](/sites/g/files/omnuum6166/files/cs288/files/2305.12638.pdf) - [ picture\_as\_pdf lessons\_from\_ictd.pdf ](/sites/g/files/omnuum6166/files/cs288/files/lessons_from_ictd.pdf) - [ picture\_as\_pdf deep\_learning\_genetic\_diseases.pdf ](/sites/g/files/omnuum6166/files/cs288/files/deep_learning_genetic_diseases.pdf) - [ picture\_as\_pdf zeroshot\_drug\_repurposing.pdf ](/sites/g/files/omnuum6166/files/cs288/files/zeroshot_drug_repurposing.pdf) ---