• Chapter 1 (“Measuring Poverty”) from: Banerjee, A.V., Benabou, R., and Mookherjee, D. (2006). Understanding Poverty. Oxford University Press, Oxford ; New York.
• Burke, M., Driscoll, A., Lobell, D., and Ermon, S. (2020). Using Satellite Imagery to Understand and Promote Sustainable Development. Science 371.
• Chi, G., Fang, H., Chatterjee, S., and Blumenstock, J. E. (2022). Microestimates of wealth for all low-and middle-income countries. Proceedings of the National Academy of Sciences, 119(3), e2113658119.
• Septiandri, A. et al. (2023). WEIRD FAccTs: How Western, Educated, Industrialized, Rich, and Democratic is FAccT? In Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’23).
• De-Arteaga, M. et al. (2018). Machine Learning for the Developing World. ACM Transactions on Management Information Systems 9.

Traditional data and measurement gaps

• Ravallion, M. (2020). On measuring global poverty. Annual Review of Economics, 12, 167-188.Can Human Development be Measured with Satellite Imagery? over

Overviews of satellite imagery in development

• Donaldson, D., Storeygard, A., 2016. The View from Above: Applications of Satellite Data in Economics. Journal of Economic Perspectives 30, 171–198. doi:10.1257/jep.30.4.171
• Khan, M. (2018). Launching into space: Using satellite imagery in financial services. Partnership for finance in a digital Africa.

Poverty mapping

• Ayush, K., Uzkent, B., Tanmay, K., Burke, M., Lobell, D., & Ermon, S. (2020). Efficient poverty mapping using deep reinforcement learning. arXiv preprint arXiv:2006.04224.
• Engstrom, R., Hersh, J.S., Newhouse, D.L., 2017. Poverty from space : using high-resolution satellite imagery for estimating economic well-being (No. WPS8284). The World Bank.
• Head, A., Manguin, M., Tran, N., Blumenstock, J.E. 2017. Can Human Development be Measured with Satellite Imagery? ICTD ’17: Proceedings of the Ninth International Conference on Information and Communication Technologies and Development.
• Henderson, J.V., Storeygard, A., Weil, D.N., 2012. Measuring Economic Growth from Outer Space. American Economic Review 102, 994–1028. doi:10.1257/aer.102.2.994
• Martinez, L. R. (2022). How much should we trust the dictator’s GDP growth estimates? Journal of Political Economy, 130(10), 2731-2769.
• Watmough, G. R., Marcinko, C. L., Sullivan, C., Tschirhart, K., Mutuo, P. K., Palm, C. A., & Svenning, J. C. (2019). Socioecologically informed use of remote sensing data to predict rural household poverty. Proceedings of the National Academy of Sciences, 116(4), 1213-1218.
• Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., Burke, M., 2020. Using publicly available satellite imagery and deep learning to understand economic well-being in Africa. Nature Communications 11, 2583. https://doi.org/10.1038/s41467-020-16185-w

Mapping built areas and infrastructure

• Cadamuro, G., Muhebwa, A., Taneja, J., 2018. Assigning a Grade: Accurate Measurement of Road Quality Using Satellite Imagery. arXiv:1812.01699
• Liu, P., Liu, X., Liu, M., Shi, Q., Yang, J., Xu, X., & Zhang, Y. (2019). Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network. Remote Sensing, 11(7), 830.
• Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., … & Dang, H. A. H. (2017). Mapping the world population one building at a time. arXiv preprint arXiv:1712.05839.

Satellite imagery in impact evaluation

• Ratledge, N., Cadamuro, G., de la Cuesta, B., Stigler, M., & Burke, M. (2022). Using machine learning to assess the livelihood impact of electricity access. Nature, 611(7936), 491-495.
• Huang, L. Y., Hsiang, S. M., & Gonzalez-Navarro, M. (2021). Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. National Bureau of Economic Research Working Paper No. 29105.
• Michler, J. D., Josephson, A., Kilic, T., & Murray, S. (2022). Privacy protection, measurement error, and the integration of remote sensing and socioeconomic survey data. Journal of Development Economics, 158, 102927.

• Bagrow, J.P., Wang, D., and Barabási, A.-L. (2012). Collective Response of Human Populations to Large-Scale Emergencies. PLoS ONE 6, e17680.
• Landau, S. (2016). Transactional information is remarkably revelatory. Proceedings of the National Academy of Sciences of the United States of America, 113(20), 5467–5469.
• Wesolowski, A., Eagle, N., Noor, A.M., Snow, R.W., and Buckee, C.O. (2012). Heterogeneous Mobile Phone Ownership and Usage Patterns in Kenya. PLOS ONE 7, e35319.
• Tai, X., Mehra, S., and Blumenstock, J. (2021). Mobile phone data reveal the effects of violence on internal displacement in Afghanistan. Nature Human Behavior 6, 624-634.
• Simko, L., Ramulu, H., Kohno, T., and Acar, Y. (2023). The Use and Non-Use of Technology During Hurricanes. Proc. ACM Hum.-Comput. Interact. 7, CSCW2, Article 366 (October 2023).

Overviews of mobile phone data in development

• Blondel, V.D., Decuyper, A., Krings, G., 2015. A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4, 10. doi:10.1140/epjds/s13688-015-0046-0
• UN Global Pulse, 2017. The State of Mobile Data for Social Good
• de Montjoye, YA., Gambs, S., Blondel, V. et al. On the privacy-conscientious use of mobile phone data. Sci Data 5, 180286 (2018).

Mobile phone use and demographics

• Blumenstock, J.E., Eagle, N., 2012. Divided We Call: Disparities in Access and Use of Mobile Phones in Rwanda. Information Technology and International Development 8, 1–16.
• Aiken, E., Thakur, V., & Blumenstock, J. (2022). Phone Sharing and Cash Transfers in Togo: Quantitative Evidence from Mobile Phone Data. In ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) (pp. 214-231).

Predicting poverty and other measures of welfare from mobile phone data

• Decuyper, A., Rutherford, A., Wadhwa, A., Bauer, J.-M., Krings, G., Gutierrez, T., Blondel, V.D., Luengo-Oroz, M.A., 2014. Estimating Food Consumption and Poverty Indices with Mobile Phone Data. arXiv preprint arXiv:1412.2595.
• Eagle, N., Macy, M., Claxton, R., 2010. Network Diversity and Economic Development. Science 328, 1029–1031. And Supplementary Materials
• Frias-Martinez, V., Virseda, J., 2012. On the relationship between socio-economic factors and cell phone usage, in: Proceedings of the Fifth International Conference on Information and Communication Technologies and Development, ICTD ’12. ACM, New York, NY, USA, pp. 76–84. doi:10.1145/2160673.2160684
• Hernandez, M., Hong, L., Frias-Martinez, V., Frias-Martinez, E., 2017. Estimating poverty using cell phone data : evidence from Guatemala (Policy Research Working Paper Series No. 7969). The World Bank.
• Jahani, E., Sundsøy, P., Bjelland, J., Bengtsson, L., Pentland, A. “Sandy”, Montjoye, Y.-A. de, 2017. Improving official statistics in emerging markets using machine learning and mobile phone data. EPJ Data Sci. 6, 3. https://doi.org/10.1140/epjds/s13688-017-0099-3
• Liang, L., Shrestha, R., Ghosh, S., Webb, P., 2020. Using mobile phone data helps estimate community-level food insecurity: Findings from a multi-year panel study in Nepal. PLOS ONE 15, e0241791. https://doi.org/10.1371/journal.pone.0241791
• Njuguna, C., McSharry, P., 2017. Constructing spatiotemporal poverty indices from big data. Journal of Business Research 70, 318–327. doi:10.1016/j.jbusres.2016.08.005
• Schmid, T., Bruckschen, F., Salvati, N., Zbiranski, T., 2017. Constructing sociodemographic indicators for national statistical institutes by using mobile phone data: estimating literacy rates in Senegal. J. R. Stat. Soc. A 180, 1163–1190. https://doi.org/10.1111/rssa.12305
• Slavchevska, V., Tyszler, M., Burra, D.D., Seymour, G., Sementsov, D., Van Lierde, A., King, B., 2021. Can call detail records provide insights into women’s empowerment? A case study from Uganda. White paper / Case Study.
• Smith-Clarke, C., Mashhadi, A., Capra, L., 2014. Poverty on the Cheap: Estimating Poverty Maps Using Aggregated Mobile Communication Networks, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’14. ACM, New York, NY, USA, pp. 511–520. doi:10.1145/2556288.2557358
• Toole JL, Lin Y-R, Muehlegger E, Shoag D, Gonzalez MC, Lazer D., 2015. Tracking Employment Shocks using Mobile Phone Data. J. R. Soc. Interface. 2015;12 (107).

Mobility, violence, and natural disasters

• Bengtsson, L., Lu, X., Thorson, A., Garfield, R., von Schreeb, J., 2011. Improved Response to Disasters and Outbreaks by Tracking Population Movements with Mobile Phone Network Data: A Post-Earthquake Geospatial Study in Haiti. PLoS Med 8, e1001083.
• Gething and Tatem (2011). Can Mobile Phone Data Improve Emergency Response to Natural Disasters? PLoS Medicine.
• Hong, L., Lee, M., Mashhadi, A., Frias-Martinez, V., 2018. Towards Understanding Communication Behavior Changes During Floods Using Cell Phone Data, in: Staab, S., Koltsova, O., Ignatov, D.I. (Eds.), Social Informatics, Lecture Notes in Computer Science. Springer International Publishing, pp. 97–107
• Lu, Xin, Linus Bengtsson, and Petter Holme. Predictability of Population Displacement after the 2010 Haiti Earthquake. Proceedings of the National Academy of Sciences 109, no. 29 (July 17, 2012): 11576–81.
• Kryvasheyeu, Y., Chen, H., Obradovich, N., Moro, E., Hentenryck, P.V., Fowler, J., Cebrian, M., 2016. Rapid assessment of disaster damage using social media activity. Science Advances 2, e1500779. doi:10.1126/sciadv.1500779
• Kapoor, A., Eagle, N., Horvitz, E., (2010) People, Quakes, and Communications: Inferences from Call Dynamics about a Seismic Event and its Influences on a Population.
• Li, T., Dejby, J., Albert, M., Bengtsson, L., Lefebvre, V., 2019. Detecting individual internal displacements following a sudden-onset disaster using time series analysis of call detail records. arXiv:1908.02377.
• Robinson, B., Power, R., Cameron, M., 2013. A sensitive Twitter earthquake detector, in: Proceedings of the 22nd International Conference on World Wide Web, WWW ’13 Companion. Association for Computing Machinery, New York, NY, USA, pp. 999–1002.
• Wilson, R. et al. (2016). Rapid and Near Real-Time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLoS Curr 8.
• Smallwood, T. R., Lefebvre, V., & Bengtsson, L. (2022). Mobile phone usage data for disaster response. Communications of the ACM, 65(4), 40-41.
• Kohli, N., Aiken, E., & Blumenstock, J. (2023). Privacy Guarantees for Personal Mobility Data in Humanitarian Response. arXiv preprint arXiv:2306.09471.

Mobility and anomaly detection

• Barnett, I., Khanna, T., Onnela, J.-P., 2016. Social and Spatial Clustering of People at Humanity’s Largest Gathering. PLOS ONE 11, e0156794.
• Young, W., Blumenstock, J.E., Fox, E., McCormick, T., 2014. Detecting and classifying anomalous behavior in spatiotemporal network data, in: The 20th ACM Conference on Knowledge Discovery and Mining (KDD ’14), Workshop on Data Science for Social Good. Presented at the Data Science for Social Good workshop at KDD, New York, NY.
• Wang, W., Kennedy, R., Lazer, D., Ramakrishnan, N., 2016. Growing pains for global monitoring of societal events. Science 353, 1502–1503.
• Dobra, A., Williams, N.E., Eagle, N., 2015. Spatiotemporal detection of unusual human population behavior using mobile phone data. PloS one 10, e0120449.
• Dong, X., Meyer, J., Shmueli, E., Bozkaya, B., Pentland, A., 2018. Methods for quantifying effects of social unrest using credit card transaction data. EPJ Data Sci. 7, 8.
• Gundogdu, D., Incel, O.D., Salah, A.A., Lepri, B., 2016. Countrywide arrhythmia: emergency event detection using mobile phone data. EPJ Data Sci. 5, 25.

Migration

• Deville, P. et al. (2014). Dynamic population mapping using mobile phone data. PNAS 111, 15888–15893. doi:10.1073/pnas.1408439111
• Blumenstock, J.E., 2012. Inferring Patterns of Internal Migration from Mobile Phone Call Records: Evidence from Rwanda. Information Technology for Development 18, 107–125.
• Blumenstock, J., Chi, G., and Tan, X., 2019. Migration and the Value of Social Networks. Working paper.
• Gonzalez, M.C., Hidalgo, C.A., Barabasi, A.-L., 2008. Understanding individual human mobility patterns. Nature 453, 779-782.
• Hong, L., Wu, J., Frias-Martinez, E., Villarreal, A., Frias-Martinez, V., 2019. Characterization of Internal Migrant Behavior in the Immediate Post-migration Period Using Cell Phone Traces, in: Proceedings of the Tenth International Conference on Information and Communication Technologies and Development, ICTD ’19. ACM, New York, NY, USA, pp. 4:1–4:12.
• Isaacman, S., Frias-Martinez, V., Frias-Martinez, E., 2018. Modeling Human Migration Patterns During Drought Conditions in La Guajira, Colombia, in: Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies, COMPASS ’18. ACM, New York, NY, USA, pp. 31:1–31:9.
• Kriendler, G., Miyauchi, Y., 2019. Measuring Commuting and Economic Activity Inside Cities with Cell Phone Records. Working Paper.
• Lenormand, M. et al. (2014). Cross-Checking Different Sources of Mobility Information. PLOS ONE 9, e105184.
• Lu, X. et al. (2016). Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh. Global Environmental Change 38, 1–7.
• Palchykov, V., Mitrović, M., Jo, H.-H., Saramäki, J., Pan, R.K., 2014. Inferring human mobility using communication patterns. Scientific Reports 4, 6174.
• Ranjan, G., Zang, H., Zhang, Z.-L., Bolot, J., 2012. Are Call Detail Records Biased for Sampling Human Mobility? SIGMOBILE Mob. Comput. Commun. Rev. 16, 33–44.
• Simini, F., González, M.C., Maritan, A., Barabási, A.-L., 2012. A universal model for mobility and migration patterns. Nature 484, 96–100.
• Song, C., Qu, Z., Blumm, N., Barabasi, A.-L., 2010. Limits of Predictability in Human Mobility. Science 327, 1018–1021.
• Zhao, Z., Shaw, S.-L., Xu, Y., Lu, F., Chen, J., Yin, L., 2016. Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science 30, 1738–1762.
• Chi, Guanghua, Lin, F., Chi, Guangqing, Blumenstock, J., 2020. A general approach to detecting migration events in digital trace data. PLOS ONE 15, e0239408.

• Christen, K. (2012). Does Information Really Want to be Free? Indigenous Knowledge Systems and the Question of Openness. International Journal of Communication, 6, 24.
• Sambasivan, N. et al. (2021). Re-imagining Algorithmic Fairness in India and Beyond. In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’21). Association for Computing Machinery, New York, NY, USA, 315–328.
• Wein, T. (2022). Dignity and Development: A Review of the Literature on Its Application, Definition, and Measurement.

• Banerjee, A., Karlan, D., Zinman, J. (2015). Six Randomized Evaluations of Microcredit: Introduction and Further Steps. American Economic Journal: Applied Economics 7, 1–21.
• Francis, E., Blumenstock, J.E., Robinson, J. (2017). Digital Credit: A Snapshot of the Current Landscape and Open Research Questions. BREAD Working Paper No. 516.
• Totolo, E. (2018). Kenya’s Digital Credit Revolution Five Years On. CGAP.
• Suri et al. (2022). Mobile Money. Vox Dev.
• Rizzi, A., Kessler, A., and Menajovsky, J. (2021). The Stories Algorithms Tell: Bias and Financial Inclusion at the Data Margins.

Digital credit scoring

• Pedro, J.S., Proserpio, D., Oliver, N., 2015. MobiScore: Towards Universal Credit Scoring from Mobile Phone Data, in: Ricci, F., Bontcheva, K., Conlan, O., Lawless, S. (Eds.), User Modeling, Adaptation and Personalization, Lecture Notes in Computer Science. Springer International Publishing, pp. 195–207.
• Shema, A., 2019. Effective Credit Scoring Using Limited Mobile Phone Data, in: Proceedings of the Tenth International Conference on Information and Communication Technologies and Development, ICTD ’19. ACM, New York, NY, USA, pp. 7:1–7:11.
• Dean, M. (2018). Can big data shape financial services in East Africa? Partnership for finance in a digital Africa.
• Nordin, K., Hunter, R., Adaii, A., 2017. Branch case study: Exploring the potential of alternative data for creating new markets. Insight2Impact Case Study.

Impacts and diffusion of digital credit

• Robinson, J., Park, D. S., & Blumenstock, J. E. (2023). The impact of digital credit in developing economies: A review of recent evidence. KDI School of Pub Policy & Management Paper.
• Björkegren, D. et al. (2022). Instant loans can lift subjective well-being: A randomized evaluation of digital credit in Nigeria. arXiv preprint arXiv:2202.13540.
• Banerjee, A., Duflo, E., Glennerster, R., Kinnan, C., 2015. The miracle of microfinance? Evidence from a randomized evaluation. American Economic Journal: Applied Economics 7, 22–53.
• Banerjee, Abhijit, Arun G. Chandrasekhar, Esther Duflo, and Matthew O. Jackson. The Diffusion of Microfinance. Science 341, no. 6144 (July 26, 2013): 1236498.
• Khan, M. (2018). Delving into human consciousness: using psychometric assessments in financial services. Partership for finance in a digital Africa.
• Bharadwaj, P., Jack, W., Suri, T., 2019. Fintech and Household Resilience to Shocks: Evidence from Digital Loans in Kenya. National Bureau of Economic Research Working Paper Series.
• Gubbins, P. & Totolo, E. (2018) Digital credit in Kenya: Evidence from demand-side surveys. Nairobi, Kenya: FSD Kenya.
• Khwaja, A.I., Mian, A., 2005. Do Lenders Favor Politically Connected Firms? Rent Provision in an Emerging Financial Market. Q J Econ 120, 1371–1411.
• Meager, R., 2019. Understanding the Average Impact of Microcredit Expansions: A Bayesian Hierarchical Analysis of Seven Randomized Experiments. American Economic Journal: Applied Economics 11, 57–91.
• Paruthi, G., Frias-Martinez, E., Frias-Martinez, V., 2016. Peer-to-peer microlending platforms: Characterization of online traits, in: 2016 IEEE International Conference on Big Data (Big Data). Presented at the 2016 IEEE International Conference on Big Data (Big Data), pp. 2180–2189.

Mobile money

• Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288-1292.
• Blumenstock, J.E., Eagle, N., Fafchamps, M., 2016. Airtime Transfers and Mobile Communications: Evidence in the Aftermath of Natural Disasters. Journal of Development Economics 120, 157–181.
• GSMA (2020). State of the Industry Report on Mobile Money.
• Khan, M.R., Blumenstock, J.E., 2016. Predictors without Borders: Behavioral Modeling of Product Adoption in Three Developing Countries. Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD `16).
• Jack, W., & Suri, T. (2014). Risk sharing and transactions costs: Evidence from Kenya’s mobile money revolution. American Economic Review, 104(1), 183-223.
• Suri, T. (2017). Mobile Money. Annual Review of Economics 9(1), 497-520.

Debit cards and ATMs

• Bachas, P., Gertler, P., Higgins, S., Seira, E., 2018. Digital financial services go a long way: Transaction costs and financial inclusion, in: AEA Papers and Proceedings. pp. 444–48.
• Clemente, R.D., Luengo-Oroz, M., Travizano, M., Xu, S., Vaitla, B., González, M.C., 2018. Sequences of purchases in credit card data reveal lifestyles in urban populations. Nature Communications 9, 3330.
• Martinez, E.A., Rubio, M.H., Martinez, R.M., Arias, J.M., Patane, D., Zerbe, A., Kirkpatrick, R., Luengo-Oroz, M., Zerbe, A., 2016. Measuring Economic Resilience to Natural Disasters with Big Economic Transaction Data. arXiv:1609.09340 [cs].
• Carpio-Pinedo, J. et al. (2022). Towards a new urban geography of expenditure: Using bank card transactions data to analyze multi-sector spatiotemporal distributions. Cities 131.

• Weitzberg, K. et al. (2021). Between Surveillance and Recognition: Rethinking Digital Identity in Aid. Big Data & Society 8(1).
• Vines, J. et al. (2013). Configuring Participation: On How We Involve People in Design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems Pp. 429–438. CHI ’13. New York, NY, USA: Association for Computing Machinery.
• Gilman, M. (2023). Democratizing AI: Principles for Meaningful Public Participation. Data & Society Policy Brief. 
• Hirsch, T., Merced, K., Narayanan, S., Imel, Z., and Atkins, D. (2017). Designing Contestability: Interaction Design, Machine Learning, and Mental Health. In Proceedings of the 2017 Conference on Designing Interactive Systems Pp. 95–99. Edinburgh United Kingdom: ACM.
• Okolo, C., Dell, N., and Vashistha, A. (2022). Making AI Explainable in the Global South: A Systematic Review. In Proceedings of the 5th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS ’22).

• Ehsan, Upol, Q. Vera Liao, Michael Muller, Mark O. Riedl, and Justin D. Weisz. (2021). Expanding Explainability: Towards Social Transparency in AI Systems. Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems: 1–19.
• Ehsan, Upol, and Mark O. Riedl. (2020). Human-Centered Explainable AI: Towards a Reflective Sociotechnical Approach. arXiv:2002.01092.
• Ananny, M., and Crawford, K. (2018). Seeing without Knowing: Limitations of the Transparency Ideal and Its Application to Algorithmic Accountability. New Media & Society 20(3): 973–989.
• Eyert, F., and Lopez, P. (2023). Rethinking Transparency as a Communicative Constellation. In 2023 ACM Conference on Fairness, Accountability, and Transparency Pp. 444–454. Chicago IL USA: ACM.

• Milusheva, S. (2020). Managing the spread of disease with mobile phone data. Journal of Development Economics 147. 
• Althoff, T., Sosič, R., Hicks, J.L., King, A.C., Delp, S.L., Leskovec, J. (2017). Large-scale physical activity data reveal worldwide activity inequality. Nature 547.
• Buckee, C. et al. (2020). Aggregated mobility data could help fight COVID-19. Science 368, 145–146.
• Wesolowski, A., Buckee, C.O., Engø-Monsen, K., Metcalf, C.J.E. (2016). Connecting Mobility to Infectious Diseases: The Promise and Limits of Mobile Phone Data. J Infect Dis. 214, S414–S420.
• Lazer, D., Kennedy, R., King, G., Vespignani, A. (2014). The parable of Google Flu: traps in big data analysis. Science 343.

Poverty and internet use

• Lampos, V., Aletras, N., Geyti, J.K., Zou, B., Cox, I.J., 2016. Inferring the Socioeconomic Status of Social Media Users Based on Behaviour and Language, in: Advances in Information Retrieval. Presented at the European Conference on Information Retrieval, Springer, Cham, pp. 689–695. See also associated blog post.
• Fatehkia, M., Tingzon, I., Orden, A., Sy, S., Sekara, V., Garcia-Herranz, M., Weber, I., 2020. Mapping socioeconomic indicators using social media advertising data. EPJ Data Sci. 9, 1–15.
• Chakraborty, S., Venkataraman, A., Jagabathula, S., Subramanian, L., 2016. Predicting Socio-Economic Indicators Using News Events, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16. ACM, New York, NY, USA, pp. 1455–1464.
• Dong, L., Chen, S., Cheng, Y., Wu, Z., Li, C., Wu, H., 2016. Measuring Economic Activities of China with Mobile Big Data. arXiv:1607.04451
• Fatehkia, M., Kashyap, R., Weber, I., 2018. Using Facebook ad data to track the global digital gender gap. World Development 107, 189–209.
• Glaeser, E.L., Kim, H., Luca, M., 2017. Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity (Working Paper No. 24010). National Bureau of Economic Research.
• Glaeser, E.L., Kominers, S.D., Luca, M., Naik, N., 2015. Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life (Working Paper No. 21778). National Bureau of Economic Research.
• Norbutas, L., Corten, R., 2017. Network structure and economic prosperity in municipalities: A large-scale test of social capital theory using social media data. Social Networks.
• Sheehan, E., Meng, C., Tan, M., Uzkent, B., Jean, N., Lobell, D., Burke, M., Ermon, S., 2019. Predicting Economic Development using Geolocated Wikipedia Articles. arXiv:1905.01627 [cs].

Contagion and spreading information over web networks

• Alatas, V., Chandrasekhar, A.G., Mobius, M., Olken, B.A., Paladines, C., 2019. When Celebrities Speak: A Nationwide Twitter Experiment Promoting Vaccination In Indonesia. National Bureau of Economic Research Working Paper Series.
• Aral, S., Nicolaides, C., 2017. Exercise contagion in a global social network. Nature Communications 8, 14753.
• Blumenstock, J.E., Keleher, N., 2015. The Price is Right? Statistical Evaluation of a Crowd-sourced Market Information System in Liberia, in: Proceedings of the 2015 Annual Symposium on Computing for Development, DEV ’15. ACM, New York, NY, USA, pp. 117–125.
• Raza, A.A., Ul Haq, F., Tariq, Z., Pervaiz, M., Razaq, S., Saif, U., Rosenfeld, R., 2013. Job Opportunities Through Entertainment: Virally Spread Speech-based Services for Low-literate Users, in: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’13. ACM, New York, NY, USA, pp. 2803–2812.

Mapping populations and mobility using web data

• Palotti, J., Adler, N., Morales-Guzman, A., Villaveces, J., Sekara, V., Garcia Herranz, M., … & Weber, I. (2020). Monitoring of the Venezuelan exodus through Facebook’s advertising platform. Plos one, 15(2), e0229175.
• Antenucci, D., Cafarella, M., Levenstein, M., Ré, C., Shapiro, M.D., 2014. Using Social Media to Measure Labor Market Flows (Working Paper No. 20010). National Bureau of Economic Research.
• Patel, N.N., Stevens, F.R., Huang, Z., Gaughan, A.E., Elyazar, I., Tatem, A.J., 2017. Improving Large Area Population Mapping Using Geotweet Densities. Trans. in GIS 21, 317–331.
• S. Spyratos, M. Vespe, F. Natale, I. Weber, E. Zagheni, M. Rango, Quantifying international human mobility patterns using Facebook Network data. PLOS ONE. 14, e0224134 (2019).

Other applications of web and social media data analysis

• Llorente, A., Garcia-Herranz, M., Cebrian, M., Moro, E., 2015. Social Media Fingerprints of Unemployment. PLOS ONE 10, e0128692. doi:10.1371/journal.pone.0128692.
• Proserpio, D., Counts, S., Jain, A., 2016. The Psychology of Job Loss: Using Social Media Data to Characterize and Predict Unemployment, in: Proceedings of the 8th ACM Conference on Web Science, WebSci ’16. ACM, New York, NY, USA, pp. 223–232.
• Goel, S., Hofman, J.M., Lahaie, S., Pennock, D.M., Watts, D.J., 2010. Predicting consumer behavior with Web search. Proceedings of the National Academy of Sciences 107, 17486–17490.
• Hjort, J., Poulsen, J., 2019. The Arrival of Fast Internet and Employment in Africa. American Economic Review 109, 1032–1079.
• Miluzzo, E., Lane, N.D., Fodor, K., Peterson, R., Lu, H., Musolesi, M., Eisenman, S.B., Zheng, X., Campbell, A.T., 2008. Sensing meets mobile social networks: the design, implementation and evaluation of the cenceme application, in: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems. pp. 337–350.
• Weidmann, N.B., Benitez-Baleato, S., Hunziker, P., Glatz, E., Dimitropoulos, X., 2016. Digital discrimination: Political bias in Internet service provision across ethnic groups. Science 353, 1151–1155.

Public health: Modeling disease spread with mobility inferred from web and phone data

• Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L., 2009. Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014. doi:10.1038/nature07634
• Choi, H., Varian, H., 2012. Predicting the Present with Google Trends. Economic Record 88, 2–9. doi:10.1111/j.1475-4932.2012.00809.x
• Olson, D.R., Konty, K.J., Paladini, M., Viboud, C., Simonsen, L., 2013. Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales. PLOS Computational Biology 9, e1003256. doi:10.1371/journal.pcbi.1003256
• Milusheva, S., 2020. Using Mobile Phone Data to Reduce Spread of Disease  Working Paper
• Oliver, N., Lepri, B., Sterly, H., Lambiotte, R., Delataille, S., Nadai, M.D., Letouzé, E., Salah, A.A., Benjamins, R., Cattuto, C., Colizza, V., Cordes, N. de, Fraiberger, S.P., Koebe, T., Lehmann, S., Murillo, J., Pentland, A., Pham, P.N., Pivetta, F., Saramäki, J., Scarpino, S.V., Tizzoni, M., Verhulst, S., Vinck, P., 2020. Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle. Science Advances
• Wesolowski, A., Metcalf, C.J.E., Eagle, N., Kombich, J., Grenfell, B.T., Bjørnstad, O.N., Lessler, J., Tatem, A.J., Buckee, C.O., 2015. Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data. PNAS 112, 11114–11119. doi:10.1073/pnas.1423542112
• Balcan, D., Colizza, V., Gonçalves, B., Hu, H., Ramasco, J.J., Vespignani, A., 2009. Multiscale mobility networks and the spatial spreading of infectious diseases. PNAS 106, 21484–21489. doi:10.1073/pnas.0906910106
• Finger, F., Genolet, T., Mari, L., Magny, G.C. de, Manga, N.M., Rinaldo, A., Bertuzzo, E., 2016. Mobile phone data highlights the role of mass gatherings in the spreading of cholera outbreaks. PNAS 113, 6421–6426. doi:10.1073/pnas.1522305113
• Salathé, Marcel, Maria Kazandjieva, Jung Woo Lee, Philip Levis, Marcus W. Feldman, and James H. Jones. A High-Resolution Human Contact Network for Infectious Disease Transmission. Proceedings of the National Academy of Sciences 107, no. 51 (December 21, 2010): 22020–25. doi:10.1073/pnas.1009094108.
• Signorini, A., Segre, A.M., Polgreen, P.M., 2011. The Use of Twitter to Track Levels of Disease Activity and Public Concern in the U.S. during the Influenza A H1N1 Pandemic. PLOS ONE 6, e19467. https://doi.org/10.1371/journal.pone.0019467
• Tatem, A.J., Huang, Z., Narib, C., Kumar, U., Kandula, D., Pindolia, D.K., Smith, D.L., Cohen, J.M., Graupe, B., Uusiku, P., Lourenço, C., 2014. Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malaria Journal 13, 52. doi:10.1186/1475-2875-13-52
• Wesolowski, A., Eagle, N., Tatem, A.J., Smith, D.L., Noor, A.M., Snow, R.W., Buckee, C.O., 2012. Quantifying the Impact of Human Mobility on Malaria. Science 338, 267–270. doi:10.1126/science.1223467
• Wesolowski, A., Qureshi, T., Boni, M.F., Sundsøy, P.R., Johansson, M.A., Rasheed, S.B., Engø-Monsen, K., Buckee, C.O., 2015. Impact of human mobility on the emergence of dengue epidemics in Pakistan. PNAS 112, 11887–11892. doi:10.1073/pnas.1504964112
• Wesolowski, A., Erbach-Schoenberg, E. zu, Tatem, A.J., Lourenço, C., Viboud, C., Charu, V., Eagle, N., Engø-Monsen, K., Qureshi, T., Buckee, C.O., Metcalf, C.J.E., 2017. Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics. Nature Communications 8, 2069.

Public health: Social distancing and movement restrictions

• Jeffrey, B., Walters, C.E., Ainslie, K.E., Eales, O., Ciavarella, C., Bhatia, S., Hayes, S., Baguelin, M., Boonyasiri, A., Brazeau, N.F., 2020. Anonymised and aggregated crowd level mobility data from mobile phones suggests that initial compliance with COVID-19 social distancing interventions was high and geographically consistent across the UK. Wellcome Open Research 5.
• Kraemer, M.U., Yang, C.-H., Gutierrez, B., Wu, C.-H., Klein, B., Pigott, D.M., Du Plessis, L., Faria, N.R., Li, R., Hanage, W.P., 2020. The effect of human mobility and control measures on the COVID-19 epidemic in China. Science 368, 493–497.
• Nouvellet, P., Bhatia, S., Cori, A., Ainslie, K.E., Baguelin, M., Bhatt, S., Boonyasiri, A., Brazeau, N.F., Cattarino, L., Cooper, L.V., 2021. Reduction in mobility and COVID-19 transmission. Nature communications 12, 1–9.
• Warren, M.S., Skillman, S.W., 2020. Mobility changes in response to COVID-19. arXiv preprint arXiv:2003.14228.
• Wellenius, G.A., Vispute, S., Espinosa, V., Fabrikant, A., Tsai, T.C., Hennessy, J., Williams, B., Gadepalli, K., Boulange, A., Pearce, A., 2020. Impacts of state-level policies on social distancing in the United States using aggregated mobility data during the COVID-19 pandemic. arXiv preprint arXiv:2004.10172.
• Peak, C.M., Wesolowski, A., zu Erbach-Schoenberg, E., Tatem, A.J., Wetter, E., Lu, X., Power, D., Weidman-Grunewald, E., Ramos, S., Moritz, S., Buckee, C.O., Bengtsson, L. Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data. Int J Epidemiol.

Public health: Other applications and data sources

• Bharti, N., Tatem, A.J., Ferrari, M.J., Grais, R.F., Djibo, A., Grenfell, B.T., 2011. Explaining Seasonal Fluctuations of Measles in Niger Using Nighttime Lights Imagery. Science 334, 1424–1427.
• Ford, T.E., Colwell, R.R., Rose, J.B., Morse, S.S., Rogers, D.J., Yates, T.L., 2009. Using Satellite Images of Environmental Changes to Predict Infectious Disease Outbreaks. Emerg Infect Dis 15, 1341–1346.
• Generous, N., Fairchild, G., Deshpande, A., Del Valle, S.Y., Priedhorsky, R., 2014. Global Disease Monitoring and Forecasting with Wikipedia. PLoS Comput Biol 10, e1003892. doi:10.1371/journal.pcbi.1003892
• De Choudhury, M., Gamon, M., Counts, S., Horvitz, E., 2013. Predicting Depression via Social Media, in: ICWSM. p. 2.
• Lampos, V., Yom-Tov, E., Pebody, R., Cox, I.J., 2015. Assessing the impact of a health intervention via user-generated Internet content. Data Min Knowl Disc 29, 1434–1457. 
• Abebe, R., Hill, S., Vaughan, J.W., Small, P.M., Schwartz, H.A., 2018. Using Search Queries to Understand Health Information Needs in Africa. arXiv:1806.05740
• Heft-Neal, S., Burney, J., Bendavid, E., Voss, K.K., Burke, M., 2020. Dust pollution from the Sahara and African infant mortality. Nature Sustainability 3, 863–871.

• Aiken, E. and Ohlenburg, T. (2023). Novel digital data sources for social protection: Opportunities and challenges. GIZ.
• Della Guardia, Anne, Milli Lake, and Pascale Schnitzer. 2022. Selective Inclusion in Cash Transfer Programs: Unintended Consequences for Social Cohesion. World Development 157: 105922. 
• Hanna, R., & Olken, B. A. (2018). Universal basic incomes versus targeted transfers: Anti-poverty programs in developing countries. Journal of Economic Perspectives, 32(4), 201-226.
• Roelen, Keetie. 2020. Receiving Social Assistance in Low- and Middle-Income Countries: Negating Shame or Producing Stigma? Journal of Social Policy 49(4): 705–723.
• Sen, A. (1992). The political economy of targeting. Washington, DC: World Bank.
• Alatas, V., Banerjee, A., Hanna, R., Olken, B. A., & Tobias, J. (2012). Targeting the poor: evidence from a field experiment in Indonesia. American Economic Review, 102(4), 1206-1240.
• Automated Neglect: How The World Bank’s Push to Allocate Cash Assistance Using Algorithms Threatens Rights. June 13, 2023. Human Rights Watch.
• Noriega-Campero, A. et al. (2020). Algorithmic targeting of social policies: fairness, accuracy, and distributed governance. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* ’20).
• Premand, P., and Schnitzer, P. (2021). Efficiency, legitimacy, and impacts of targeting methods: Evidence from an experiment in Niger. The World Bank Economic Review, 35(4), 892-920.
• Wein, Tom, Heather Lanthorn, and Torben Fischer. 2023. First Steps toward Building Respectful Development: Three Experiments on Dignity in Aid in Kenya and the United States. World Development Perspectives 29: 100485.

• Abelson, B., Varshney, K. R., & Sun, J. (2014, August). Targeting direct cash transfers to the extremely poor. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1563-1572).
• Lindert, K., Karippacheril, T. G., Caillava, I. R., & Chávez, K. N. (Eds.). (2020). Sourcebook on the foundations of social protection delivery systems. World Bank Publications.
• Mukherjee, A. N., Bermeo Rojas, L. X., Okamura, Y., Muhindo, J. V., & Bance, P. G. (2023). Digital-first Approach to Emergency Cash Transfers: STEP-KIN in the DemocraticRepublic of Congo (No. 181798). The World Bank.
• Smythe, I. S., & Blumenstock, J. E. (2022). Geographic microtargeting of social assistance with high-resolution poverty maps. Proceedings of the National Academy of Sciences, 119(32), e2120025119.

• Seltzer, W. (2006). The Dark Side of Numbers: Updated. In Bevölkerungsforschung und Politik in Deutschland im 20. Jahrhundert. Rainer Mackensen, ed. Pp. 119–136. Wiesbaden: VS Verlag für Sozialwissenschaften.
• de Montjoye, Y., Hidalgo, C., Verleysen, M., and Blondel, V. (2013). Unique in the Crowd: The privacy bounds of human mobility. Scientific Reports.
• Ahmed, S. I., Haque, R., Chen, J., and Dell, N. (2017). Digital Privacy Challenges with Shared Mobile Phone Use in Bangladesh. Proceedings of the ACM on Human-Computer Interaction 1(CSCW): 1–20.
• Blumenstock, J. and Kohli, N. (2023). Big Data Privacy in Emerging Market Fintech and Financial Services: A Research Agenda. arXiv preprint.
• [Particularly introduction, conclusion, and chapter 7] Scott, J. (1998). Seeing like a state.

• Birhane, A. (2020). Algorithmic Colonization of Africa. SCRIPT-Ed 17(2): 389–409. 
• Cooper, F. (2010). Writing the History of Development. Journal of Modern European History 8(1). Verlag C.H.Beck: 5–23.
• Cobb, C. et al. (2016). Computer Security for Data Collection Technologies. In Proceedings of the Eighth International Conference on Information and Communication Technologies and Development (ICTD ’16).
• Donovan, K. (2018). Financial Inclusion Means Your Money Isn’t With You: Conflicts Over Social Grants and Financial Services in South Africa. In IV Small, S. Musaraj and B. Maurer (eds), Money at the Margins: Global Perspectives on Technology, Inclusion and Design.

• Belmont Report
• CARE Principles
• FAIR Principles
• Human rights for development
• Green, B. (2019). ‘Good’ Isn’t good enough. NeurIPS workshop on AI for social good.
• Elsayed-Ali, S. (2019). In Response to “Good” Isn’t Enough.
• Dogan, A. and Wood, D. (2023). “Do you collect data to give to the university or do you do the work to benefit people?”: Indigenous Data Sovereignty in Environmental Contexts. In Proceedings of the 6th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS ’23).
• Png, M. (2022). At the Tensions of South and North: Critical Roles of Global South Stakeholders in AI Governance. In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT ’22).

• Selbst, A D. and Barocas, S, (2018). The Intuitive Appeal of Explainable Machines. 87 Fordham Law Review 1085.
• Mitchell, M. et al. (2019.) Model Cards for Model Reporting. In Proceedings of the Conference on Fairness,
• Microsoft’s Responsible AI Assessment (click link that says “Responsible AI Impact Assessment Guide”)
• Microsoft’s Transparency Note (read for structure, not for specific content)

• Nolte, C. (2020). High-resolution land value maps reveal underestimation of conservation costs in the United States. Proceedings of the National Academy of Sciences 117, 29577–29583.
• Fang, F., Stone, P., and Tambe, M. (2015). When Security Games Go Green: Designing Defender Strategies to Prevent Poaching and Illegal Fishing. In IJCAI (pp. 2589-2595).
• Burke, M., Lobell, D.B., 2017. Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences 201616919.
• Tuia, D. et al. (2022). Perspectives in machine learning for wildlife conservation. Nature communications, 13(1), 792.
• Kerner, H. et al. (2020). Resilient In-Season Crop Type Classification in Multispectral Satellite Observations using Growth Stage Normalization. arXiv preprint.
• Xu, L. et al. (2020). Stay Ahead of Poachers: Illegal Wildlife Poaching Prediction and Patrol Planning Under Uncertainty with Field Test Evaluations. IEEE International Conference on Data Engineering.
• Guo, R. et al. (2020). Enhancing Poaching Predictions for Under-Resourced Wildlife Conservation Parks Using Remote Sensing Imagery. ML4D.
• Bondi, E. et al. (2018). SPOT Poachers in Action: Augmenting Conservation Drones with Automatic Detection in Near Real Time. Proceedings of the 30th Annual Conference on Innovative Applications of Artificial Intelligence (Emerging Applications Track).
• Guirado, E. et al. (2019). Whale counting in satellite and aerial images with deep learning. Nature Scientific Reports.

• Rolf, E. et al. (2020). Balancing competing objectives with noisy data: Score-based classifiers for welfare-aware machine learning. Proceedings of the 37th International Conference on Machine Learning.
• Bjorkegren, D. et al. (2020). Manipulation-proof machine learning. arXiv preprint.
• Ratlege et al. (2022). Using machine learning to assess the livelihood impact of electricity access. Nature 611.
• Huang et al. (2021). Using satellite imagery and deep learning to evaluate the impact of anti-poverty programs. NBER Working Paper.
• Aiken et al. (2023). Estimating impact with surveys vs. digital traces: Evidence from randomized cash transfers in Togo. NBER Working Paper.
• Angelopoulos et al. (2023). Prediction-powered inference. Science 382.