{ "id": "AIBD-0093", "title": "Togo's Novissi program targeted rural emergency cash with machine learning on satellite imagery and mobile-phone data", "summary": "ML on satellite imagery and phone metadata set eligibility for Novissi rural cash; 138,531 beneficiaries paid, Nov 2020 to Aug 2021", "date_observed": "2020-11-02", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "The Government of Togo launched the Novissi emergency cash program on 8 April 2020. On 2 November 2020 it extended it to rural areas using a machine-learning pipeline built with UC Berkeley and IPA researchers. Satellite imagery produced poverty estimates for every canton to rank the poorest. Within eligible cantons a model trained on mobile-phone metadata predicted each subscriber's consumption to decide who was paid. Beneficiaries self-registered by USSD and received mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men). The World Bank and Government of Togo report 138,531 rural beneficiaries from November 2020 to August 2021, funded by GiveDirectly (US$10 million). The Nature evaluation found phone-based targeting reduced exclusion errors by 4–21% relative to geographic alternatives.", "category": "benefit created", "unexpected": false, "purpose": "Prioritize the poorest cantons and the poorest mobile subscribers within them for COVID-19 emergency cash transfers in the absence of a social registry.", "system": "Novissi rural targeting pipeline: satellite-imagery poverty maps for canton ranking plus supervised machine learning on anonymized mobile-phone call detail records for individual eligibility", "model_version": "gradient boosting regressor with Microsoft's LightGBM", "developer": "Innovations for Poverty Action, CEGA and UC Berkeley (Aiken and Blumenstock, UC Berkeley; Bellue, University of Mannheim; Karlan and Udry, Northwestern University) for the Government of Togo", "deployer": "Government of Togo, Ministry of Digital Economy and Transformation, with GiveDirectly", "location": "Rural cantons, Togo", "domain": "Social protection and humanitarian cash transfers", "segment": "Government social assistance", "benefit_type": "Increased Scalability", "taxonomy_note": "The good for beneficiaries is emergency cash, and the type names the program's reach into rural areas with no registry.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "Five mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men) to each of 138,531 rural beneficiaries during COVID-19 restrictions (World Bank study, Table 1)." }, { "group": "organizational", "depth": 2, "reason": "The Government of Togo delivered shock-responsive rural assistance without a social registry. The World Bank documents the model as a reusable case study." }, { "group": "societal", "depth": 3, "reason": "Material to many: 138,531 rural beneficiaries reached, and the Nature evaluation reports exclusion errors 4–21% lower than geographic targeting." } ], "ai_role": { "status": "assessed", "statement": "Decisive for who was paid: canton ranking and individual eligibility were both set by machine-learning predictions, while humans fixed the budget, thresholds and registration rules. The Nature evaluation is co-authored by the team that built the targeting method. The World Bank/Government of Togo case study is independent of the developers." }, "evidence": [ { "kind": "peer-reviewed study", "citation": "Aiken, E., Bellue, S., Karlan, D., Udry, C., Blumenstock, J. E. (2022). Machine learning and phone data can improve targeting of humanitarian aid. Nature 603, 864–870 (the quotation is the Extended Data Fig. 1 caption, panel a, with its reference markers omitted).", "title": "Machine learning and phone data can improve targeting of humanitarian aid", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/", "date": "2022-03-16", "written_by": "makers", "quote": "Satellite imagery of Togo is used to construct micro-estimates of poverty (middle), which are overlayed with population data to produce canton-level estimates of wealth." }, { "kind": "report", "citation": "Lawson, C., Koudeka, M., Cárdenas Martínez, A. L., Alberro Encinas, L. I., George Karippacheril, T. (2023). Novissi Togo: Harnessing Artificial Intelligence to Deliver Shock-Responsive Social Protection. World Bank and Government of Togo, Social Protection and Jobs Discussion Paper No. 2306, September 2023 (month only; first day used).", "title": "Novissi Togo: Harnessing Artificial Intelligence to Deliver Shock-Responsive Social Protection", "url": "https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf", "date": "2023-09-01", "written_by": "deployer", "quote": "Subsequently, the program was extended on November 2, 2020, to rural areas with a high prevalence of poverty (henceforth referred to as model 2)." }, { "kind": "press release", "citation": "GiveDirectly (2021). Innovations in Togo and the future of cash distribution. GiveDirectly, The Future of Cash, published 20 April 2021 (implementing partner and funder, not the AI developer).", "title": "Innovations in Togo and the future of cash distribution", "url": "https://www.givedirectly.org/togo/", "date": "2021-04-20", "written_by": "deployer", "quote": "Leveraging machine learning in combination with satellite imagery and cell phone metadata, the program was able to identify those living in extreme poverty" } ], "replication": { "produced_by": [ "Satellite-imagery poverty map ranking cantons", "Consumption model trained on mobile-phone metadata with a recent phone survey as ground truth", "USSD self-registration and mobile-money payment rails", "Government and GiveDirectly funding decision" ], "must_hold": [ "Target population owns and uses mobile phones (non-subscribers are excluded by design)", "Operators provide anonymized call detail records", "Recent ground-truth survey available for training", "Mobile-money infrastructure in place" ], "echoes": [ { "date": "2021-01-01", "setting": "Nigeria Rapid Response Register: ML poverty maps from satellite imagery selected 1,163 poorest urban wards for COVID-19 cash (month only known; first day used)", "model_version": null, "outcome": "recurred", "source": "Date: Human Rights Watch (2021) 'began in January 2021', https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living, and Smythe and Blumenstock (2022) PNAS 'mid-January 2021', https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/; ward count: World Bank (2021) PAD P176935, https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf (the PAD itself dates the USSD self-registration launch to June 2021)" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "546", "title": "Algorithm to Distribute Social Welfare Reported for Oversimplifying Economic Vulnerability", "url": "https://incidentdatabase.ai/cite/546", "severity": null, "group": "personal", "basis": "World Bank-backed poverty-targeting algorithm for cash transfers oversimplified household vulnerability, excluding poor Jordanians; Novissi used the same machine-learning targeting of the poor." }, "headline": "Novissi targeting pays 138,531 rural beneficiaries in Togo", "metric": "reduced exclusion errors by 4–21% relative to geographic alternatives", "comparator": { "against": "the geographic targeting options considered by the Government of Togo", "before": "errors of exclusion of 59%–78% for the feasible alternatives", "after": "errors of exclusion of 53% for the phone-based approach, a reduction of 4–21%", "basis": "measured", "quote": "Relative to the geographic targeting options considered by the Government of Togo, the machine-learning approach reduces errors of exclusion by 4–21%.", "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/" }, "later": null, "value": null, "developer_country": "United States", "deployer_country": "Togo", "unrated_because": null, "brief_notes": null, "brief": null, "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "source": null, "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 546 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Corrected the GiveDirectly item to its printed title and 20 April 2021 date, marked the World Bank discussion paper as month-only, restored “(middle)” to the Nature quote and labeled it as the Extended Data Fig. 1 caption, re-stated the developer team from the Nature affiliations (Bellue at Mannheim, Karlan and Udry at Northwestern) and the World Bank's IPA/CEGA/UC Berkeley description, reworded summary and headline because the ML set eligibility while 138,531 is the count of registrants validated and paid, and cited HRW and PNAS rather than the PAD for the January 2021 Nigeria echo date since the PAD gives June 2021.", "source": "https://www.givedirectly.org/togo/; https://www.ebi.ac.uk/europepmc/webservices/rest/PMC8967719/fullTextXML; https://documents.worldbank.org/curated/en/099751009222330502/pdf/IDU0e83f857301ff1047bf082710a8d21ddf42c3.pdf; https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Added what the benefit is measured against, the model version from the cited sources.", "source": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/; https://pmc.ncbi.nlm.nih.gov/articles/PMC8967719/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Increased Scalability under Trustible's full taxonomy of 23 types; it was Improved Accessibility among the six first used.", "source": "https://trustible.ai/post/a-framework-for-measuring-the-benefits-of-ai/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Country of the developer and the deployer added from the organization's own site or a registry.", "source": "https://poverty-action.org/contact-us; https://numerique.gouv.tg/", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" }, { "date": "2026-10-22", "change": "Organization name standardized to Innovations for Poverty Action.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' peer-reviewed study, the deployer's report and the deployer's press release. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0093", "catalog_version": "1.0.0", "source_fingerprint": "77448671472da44e0b4790a875da6f9da6fe878d11942d8dda8a6b9513fc8e36", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0093-source-0001", "AIBD-0093-source-0002", "AIBD-0093-source-0003" ], "families": { "strategic assessments": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "evaluations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "monetary observations": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Innovations for Poverty Action, CEGA and UC Berkeley (Aiken and Blumenstock, UC Berkeley; Bellue, University of Mannheim; Karlan and Udry, Northwestern University) for the Government of Togo", "deployer": "Government of Togo, Ministry of Digital Economy and Transformation, with GiveDirectly" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "developer", "deployer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "actors[].ownership_control": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "ai_contribution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ai_role": { "status": "assessed", "statement": "Decisive for who was paid: canton ranking and individual eligibility were both set by machine-learning predictions, while humans fixed the budget, thresholds and registration rules. The Nature evaluation is co-authored by the team that built the targeting method. The World Bank/Government of Togo case study is independent of the developers." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "ai_relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "category": "benefit created", "ai_role": { "status": "assessed", "statement": "Decisive for who was paid: canton ranking and individual eligibility were both set by machine-learning predictions, while humans fixed the budget, thresholds and registration rules. The Nature evaluation is co-authored by the team that built the targeting method. 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On 2 November 2020 it extended it to rural areas using a machine-learning pipeline built with UC Berkeley and IPA researchers. Satellite imagery produced poverty estimates for every canton to rank the poorest. Within eligible cantons a model trained on mobile-phone metadata predicted each subscriber's consumption to decide who was paid. Beneficiaries self-registered by USSD and received mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men). The World Bank and Government of Togo report 138,531 rural beneficiaries from November 2020 to August 2021, funded by GiveDirectly (US$10 million). 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No new source review is claimed.", "record_fields": [ "model_version", "system", "ai_role" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].model_version": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "model_version": "gradient boosting regressor with Microsoft's LightGBM" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "model_version" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Exact release not established.", "editor_note": "" } }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Novissi rural targeting pipeline: satellite-imagery poverty maps for canton ranking plus supervised machine learning on anonymized mobile-phone call detail records for individual eligibility" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "system" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].provider": { "state": "value_present", "value": { "kind": "structured", "data": { "application_developer": "Innovations for Poverty Action, CEGA and UC Berkeley (Aiken and Blumenstock, UC Berkeley; Bellue, University of Mannheim; Karlan and Udry, Northwestern University) for the Government of Togo" } }, "provenance": { "kind": "legacy_record", "basis": "Mapped from the existing case description without a new external source review.", "record_fields": [ "developer" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "taxonomy_crosswalks[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "title": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "title": "Togo's Novissi program targeted rural emergency cash with machine learning on satellite imagery and mobile-phone data", "headline": "Novissi targeting pays 138,531 rural beneficiaries in Togo" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "title", "headline" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "version": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "77448671472da44e0b4790a875da6f9da6fe878d11942d8dda8a6b9513fc8e36" ], "role": "SHA-256 of normalized original case data" }, "provenance": { "kind": "curatorial", "basis": "Deterministic content version assigned by the local migration.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "workflow": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "The Government of Togo launched the Novissi emergency cash program on 8 April 2020. On 2 November 2020 it extended it to rural areas using a machine-learning pipeline built with UC Berkeley and IPA researchers. Satellite imagery produced poverty estimates for every canton to rank the poorest. Within eligible cantons a model trained on mobile-phone metadata predicted each subscriber's consumption to decide who was paid. Beneficiaries self-registered by USSD and received mobile-money installments of 8,170 CFA (women) or 7,000 CFA (men). The World Bank and Government of Togo report 138,531 rural beneficiaries from November 2020 to August 2021, funded by GiveDirectly (US$10 million). The Nature evaluation found phone-based targeting reduced exclusion errors by 4–21% relative to geographic alternatives.", "replication": { "produced_by": [ "Satellite-imagery poverty map ranking cantons", "Consumption model trained on mobile-phone metadata with a recent phone survey as ground truth", "USSD self-registration and mobile-money payment rails", "Government and GiveDirectly funding decision" ], "must_hold": [ "Target population owns and uses mobile phones (non-subscribers are excluded by design)", "Operators provide anonymized call detail records", "Recent ground-truth survey available for training", "Mobile-money infrastructure in place" ], "echoes": [ { "date": "2021-01-01", "setting": "Nigeria Rapid Response Register: ML poverty maps from satellite imagery selected 1,163 poorest urban wards for COVID-19 cash (month only known; first day used)", "outcome": "recurred", "source": "Date: Human Rights Watch (2021) 'began in January 2021', https://www.hrw.org/report/2021/07/28/between-hunger-and-the-virus/the-impact-of-the-covid-19-pandemic-on-people-living, and Smythe and Blumenstock (2022) PNAS 'mid-January 2021', https://pmc.ncbi.nlm.nih.gov/articles/PMC9371736/; ward count: World Bank (2021) PAD P176935, https://documents.worldbank.org/curated/en/717321706545772846/pdf/Nigeria-National-Social-Safety-Net-Program-Scale-Up-Project.pdf (the PAD itself dates the USSD self-registration launch to June 2021)" } ] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } }, "summary": { "total": 91, "addressed": 91, "known": 34, "reviewed": 0, "record_mapped": 34, "not_assessed": 57, "complete": false, "by_state": { "value_present": 34, "not_reported": 0, "unresolved": 0, "not_assessed": 57, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }