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AI for Impact: Data Monitoring & Evaluation in Development

Reading list

Every reading this course assigns, module by module, with the best free copy we could find.

59 readings33 free to read12 on request14 books

A free copy is the published text, or the author's own manuscript held by a university repository, which has the same content without the journal's layout. A subscription reading opens at the publisher, where your library may give you access. If it does not, request a copy and we will reply by email. Books link to their publisher.

Module 1. The AI-M&E Landscape

  1. Mathieu, E., Giattino, C., Samborska, V. and Roser, M. (2023). Artificial Intelligence. Our World in Data.Read it free
  2. Björkegren, D. and Grissen, D. (2020). Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment. World Bank Economic Review, 34(3), 618-634.Read it free
  3. Aiken, E., Bellue, S., Karlan, D., Udry, C. and Blumenstock, J. E. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature, 603, 864-870.Read it free
  4. Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.Publisher pageBook
  5. O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.Publisher pageBook

Module 2. Needs Assessment for AI Integration

  1. Principles for Digital Development (2024). Understand the Existing Ecosystem. Digital Impact Alliance.Read it free
  2. Banerjee, A., Chandrasekhar, A. G., Dalpath, S., Duflo, E., Floretta, J., Jackson, M. O., Kannan, H., Loza, F., Sankar, A., Schrimpf, A. and Shrestha, M. (2021). Selecting the Most Effective Nudge: Evidence from a Large-Scale Experiment on Immunization. NBER Working Paper 28726.Read it free
  3. Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.Publisher pageSubscription
  4. Gebru, T., Morgenstern, J., Vecchione, B., Wortman Vaughan, J., Wallach, H., Daumé III, H. and Crawford, K. (2021). Datasheets for Datasets. Communications of the ACM, 64(12), 86-92.Read it free
  5. Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P. and Aroyo, L. M. (2021). "Everyone wants to do the model work, not the data work": Data Cascades in High-Stakes AI. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, 1-15.Publisher pageSubscription

Module 3. AI for Data Collection

  1. World Bank (2021). World Development Report 2021: Data for Better Lives. World Bank.Read it free
  2. Blumenstock, J., Cadamuro, G. and On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073-1076.Publisher pageSubscription
  3. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B. and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794.Publisher pageSubscription
  4. Grimmer, J., Roberts, M. E. and Stewart, B. M. (2022). Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton University Press.Publisher pageBook
  5. Manning, C. D. and Schütze, H. (1999). Foundations of Statistical Natural Language Processing. MIT Press.Publisher pageBook

Module 4. Computer Vision & Geospatial Analysis

  1. Xie, M., Jean, N., Burke, M., Lobell, D. and Ermon, S. (2016). Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1).Read it free
  2. Burke, M., Driscoll, A., Lobell, D. B. and Ermon, S. (2021). Using satellite imagery to understand and promote sustainable development. Science, 371(6535), eabe8628.Publisher pageSubscription
  3. Donaldson, D. and Storeygard, A. (2016). The View from Above: Applications of Satellite Data in Economics. Journal of Economic Perspectives, 30(4), 171-198.Read it free
  4. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B. and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794.Publisher pageSubscription

Module 5. NLP for Qualitative Data

  1. Blei, D. M., Ng, A. Y. and Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research, 3, 993-1022.Read it free
  2. Gentzkow, M., Kelly, B. and Taddy, M. (2019). Text as Data. Journal of Economic Literature, 57(3), 535-574.Read it free
  3. Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Albertson, B. and Rand, D. G. (2014). Structural Topic Models for Open-Ended Survey Responses. American Journal of Political Science, 58(4), 1064-1082.Publisher pageSubscription
  4. Athey, S. (2017). Beyond prediction: Using big data for policy problems. Science, 355(6324), 483-485.Publisher pageSubscription
  5. Mullainathan, S. and Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87-106.Read it free

Module 6. Algorithmic Targeting & Beneficiary Selection

  1. Barocas, S., Hardt, M. and Narayanan, A. (2023). Fairness and Machine Learning: Limitations and Opportunities. MIT Press.Read it free
  2. Hanna, R. and Olken, B. A. (2018). Universal Basic Incomes versus Targeted Transfers: Anti-Poverty Programs in Developing Countries. Journal of Economic Perspectives, 32(4), 201-226.Read it free
  3. Aiken, E., Bellue, S., Karlan, D., Udry, C. and Blumenstock, J. E. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature, 603, 864-870.Read it free
  4. Bommasani, R. et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv:2108.07258. Stanford Center for Research on Foundation Models.Read it free
  5. Brown, T. B. et al. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877-1901.Read it free

Module 7. Real-Time Monitoring & Anomaly Detection

  1. Pang, G., Shen, C., Cao, L. and van den Hengel, A. (2021). Deep Learning for Anomaly Detection: A Review. ACM Computing Surveys, 54(2), 38:1-38:38.Read it free
  2. Hilbert, M. (2013). Big Data for Development: From Information- to Knowledge Societies. SSRN Working Paper 2205145.Read it free
  3. UN Global Pulse (2012). Big Data for Development: Challenges & Opportunities. United Nations Global Pulse.Read it free
  4. Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.Publisher pageBook
  5. Floridi, L. (2023). The Ethics of Artificial Intelligence: Principles, Challenges, and Opportunities. Oxford University Press.Publisher pageBook

Module 8. AI for Adaptive Programming

  1. Andrews, M., Pritchett, L. and Woolcock, M. (2017). Building State Capability: Evidence, Analysis, Action. Oxford University Press.Read it free
  2. Andrews, M., Pritchett, L. and Woolcock, M. (2013). Escaping Capability Traps Through Problem Driven Iterative Adaptation (PDIA). World Development, 51, 234-244.Publisher pageSubscription
  3. Pritchett, L., Woolcock, M. and Andrews, M. (2013). Looking Like a State: Techniques of Persistent Failure in State Capability for Implementation. Journal of Development Studies, 49(1), 1-18.Read it free
  4. Varian, H. R. (2014). Big Data: New Tricks for Econometrics. Journal of Economic Perspectives, 28(2), 3-28.Read it free
  5. McKinney, W. (2017). Python for Data Analysis, 2nd edition. O'Reilly Media.Publisher pageBook

Module 9. The Limits of AI in Causal Inference

  1. Hernán, M. A. and Robins, J. M. (2020). Causal Inference: What If. Chapman & Hall/CRC.Read it free
  2. Athey, S. and Imbens, G. W. (2019). Machine Learning Methods That Economists Should Know About. Annual Review of Economics, 11, 685-725.Read it free
  3. Mullainathan, S. and Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87-106.Read it free
  4. Blumenstock, J., Cadamuro, G. and On, R. (2015). Predicting poverty and wealth from mobile phone metadata. Science, 350(6264), 1073-1076.Publisher pageSubscription
  5. Toole, J. L., Lin, Y.-R., Muehlegger, E., Shoag, D., González, M. C. and Lazer, D. (2015). Tracking employment shocks using mobile phone data. Journal of the Royal Society Interface, 12(107), 20150185.Free copy, a working-paper versionPublisher page

Module 10. Ethics, Bias & Accountability

  1. UNESCO (2021). Recommendation on the Ethics of Artificial Intelligence. UNESCO.Read it free
  2. Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.Publisher pageBook
  3. O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown.Publisher pageBook
  4. Burke, M. and Lobell, D. B. (2017). Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences, 114(9), 2189-2194.Publisher pageSubscription
  5. Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B. and Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794.Publisher pageSubscription

Module 11. Context Assessment: Case Studies

  1. Roser, M., Ritchie, H. and Mathieu, E. (2023). Technological Change. Our World in Data.Read it free
  2. Banerjee, A. V. and Duflo, E. (2011). Poor Economics: A Radical Rethinking of the Way to Fight Global Poverty. PublicAffairs.Publisher pageBook
  3. Deaton, A. (2013). The Great Escape: Health, Wealth, and the Origins of Inequality. Princeton University Press.Publisher pageBook
  4. Andrews, M., Pritchett, L. and Woolcock, M. (2017). Building State Capability: Evidence, Analysis, Action. Oxford University Press.Read it free
  5. Ramalingam, B. (2013). Aid on the Edge of Chaos: Rethinking International Cooperation in a Complex World. Oxford University Press.Publisher pageBook

Module 12. Strategy Building: Build vs. Buy, Pilot Design, Sustainability

  1. World Bank (2016). World Development Report 2016: Digital Dividends. World Bank.Read it free
  2. World Bank (2021). World Development Report 2021: Data for Better Lives. World Bank.Read it free
  3. Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S. D., Tegmark, M. and Fuso Nerini, F. (2020). The role of artificial intelligence in achieving the Sustainable Development Goals. Nature Communications, 11, 233.Read it free
  4. Crawford, K. (2021). Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence. Yale University Press.Publisher pageBook
  5. Eubanks, V. (2018). Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor. St. Martin's Press.Publisher pageBook

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