{ "id": "AIBD-0095", "title": "GiveDirectly paid anticipatory cash in Kogi State, Nigeria, on Google Flood Hub forecast triggers (August to October 2024)", "summary": "Google Flood Hub forecast triggers paid $105 to 4,600 people in Ibaji, Kogi State, mostly before floods peaked on 21 October 2024", "date_observed": "2024-08-29", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "In 2024 GiveDirectly ran an anticipatory cash program in six wards of Ibaji, Kogi State, Nigeria. JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation appeared.", "category": "protective response", "unexpected": false, "purpose": "Trigger and target anticipatory cash transfers to flood-prone communities before river flooding.", "system": "Google Flood Hub AI river-flood forecasts accessed via API, with JBA Global Resilience hydrology analysis and a 20%-of-community-area trigger rule", "model_version": null, "developer": "Google (Flood Hub); trigger design by GiveDirectly with JBA Global Resilience", "deployer": "GiveDirectly", "location": "Ibaji, Kogi State, Nigeria", "domain": "Humanitarian anticipatory action and cash transfers", "segment": "NGO cash assistance", "benefit_type": "Reduced Risk", "taxonomy_note": "Cash paid before a forecast flood is read as reducing potential for harm; Trustible's description stresses legal harm.", "registry_ids": {}, "ratings": [ { "group": "personal", "depth": 2, "reason": "$105 to over 4,600 individuals, followed by a $210 transfer (recipient count not stated). Implementer survey reports weekly income doubling and fewer food shortages (self-reported, not independently verified)." }, { "group": "organizational", "depth": 1, "reason": "GiveDirectly gained a tested trigger pipeline and documented lessons. It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ], "ai_role": { "status": "assessed", "statement": "Decisive for timing and geography: GiveDirectly told The New Humanitarian that satellite/AI flood modeling determined which communities were paid and when. Individual eligibility was decided by non-AI verification (ward residence, Smile ID checks, call-center calls). The investigation identifies that as the main source of exclusions. GiveDirectly's own page attributes the late payments to identity checks in two wards." }, "evidence": [ { "kind": "press release", "citation": "GiveDirectly (2025). How AI helped send cash just before floods hit in Nigeria. GiveDirectly (implementer report; page updated 2026).", "title": "How AI helped send cash just before floods hit in Nigeria", "url": "https://www.givedirectly.org/flood-forecast-ai/", "date": "2025-06-27", "written_by": "deployer", "quote": "We sent early payments to 3,250 people between August 29 and October 6" }, { "kind": "news report", "citation": "Ayeni, D. (2026). GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria. The New Humanitarian, investigation, 31 August 2026 (live page returns 403).", "title": "GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria", "url": "https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "date": "2026-08-31", "written_by": "independent", "quote": "Would-be aid recipients said they were excluded for submitting personal data that did not perfectly match government or bank records" }, { "kind": "news report", "citation": "Cheng, K.-C. (2024). Could AI save Nigerians from devastating floods? Al Jazeera.", "title": "Could AI save Nigerians from devastating floods?", "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "date": "2024-09-21", "written_by": "independent", "quote": "Omali received his first transfer of 177,866 naira on August 31 this year." } ], "replication": { "produced_by": [ "Google Flood Hub forecasts via API", "20%-of-area trigger rule defined by GiveDirectly and JBA", "USSD pre-enrollment, Smile ID verification and call-center checks", "Cash transfers using recipients' self-reported bank details. GiveDirectly and the IRC received a $4.6 million Google.org grant in 2024 for AI-assisted anticipatory cash (The New Humanitarian). It is not stated to have funded the Kogi transfers" ], "must_hold": [ "Forecast skill on the Niger and Benue floodplain", "Phone access, charging and matching ID records among the poorest", "Funds released and verification completed before the hazard" ], "echoes": [ { "date": "2022-10-01", "setting": "GiveDirectly's 2022 Kogi State response: communities chosen before the season by layering historical flood data (Floodbase, Fathom), granular poverty maps and crop and village density derived from satellite imagery; people enrolled mid-September 2022 and paid once October 2022 flooding began (month only known; first day used)", "model_version": null, "outcome": "unknown", "source": "https://www.givedirectly.org/flood-pilots/" }, { "date": null, "setting": "GiveDirectly anticipatory programs in Kenya, Mozambique and a planned Bangladesh RCT (Jamuna basin)", "model_version": null, "outcome": "unknown", "source": "The New Humanitarian (2026), https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria" } ] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "severity": null, "group": "societal", "basis": "Google-built hazard alert system underestimated the earthquake and failed to warn residents; Flood Hub triggers from the same developer decided who received anticipatory cash." }, "headline": "Google Flood Hub triggers pay $105 to 4,600 people in Kogi", "metric": "weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%", "comparator": { "against": "recipients' own situation before the transfers (GiveDirectly follow-up survey; no control group)", "before": "weekly income ₦4,500; 97% facing food shortages", "after": "weekly income ₦10,000; 7% facing food shortages", "basis": "measured", "quote": "Weekly income more than doubled, from ₦4,500 to ₦10,000 🍲 Food security improved, with 97% facing food shortages before and just 7% after", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "later": { "status": "scaled", "date": "2025-06-27", "note": "GiveDirectly's implementer report (published 27 June 2025, revised July 2026 to add the tradeoffs of remote phone-based targeting) says it is extending AI-triggered anticipatory cash with Google Research to new countries and other hazards.", "quote": "GiveDirectly continues to work with Google Research to scale the use of AI for anticipatory action to new countries and explore anticipatory cash transfers for other hazards, such as droughts.", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "value": { "amount": "$105", "what": "anticipatory cash transfer per recipient before the flood peak (a second $210 transfer followed)", "who": "over 4,600 flood-exposed residents of Ibaji, Kogi State", "estimate": false, "quote": "Once forecasted flood thresholds were met, we sent $105 to over 4,600 individuals before the flood peak.", "url": "https://www.givedirectly.org/flood-forecast-ai/" }, "developer_country": "United States", "deployer_country": "United States", "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 1546 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Re-aligned the account, ratings, AI-role statement, what produced it and the 2022 echo with the GiveDirectly and New Humanitarian texts (JBA pulled forecasts by API and set thresholds while GiveDirectly monitored daily, the $210 recipient count is not stated, the page was updated in July 2026 before the 31 August 2026 investigation, payments used bank details rather than mobile money and the Google.org grant is not tied to Kogi, satellite imagery informed crop and village density rather than poverty maps, late payments are the page's identity checks), confirmed the Ayeni byline against the Wayback snapshot of 22 September 2026, and dropped the word “State” from the headline to bring it under the 64-character limit.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-pilots/; https://web.archive.org/web/20260922092208/https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk 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": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-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://en.wikipedia.org/wiki/Google; https://projects.propublica.org/nonprofits/organizations/271661997", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the replication conditions, 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 Google.", "source": null, "editor": "AIBD" }, { "date": "2026-10-25", "change": "Clarified the displayed field answers and placed measurements and limitations in their respective fields.", "source": null, "editor": "AIBD" } ], "confidence": "reported", "shade": "medium", "claim_status": "Independently reported", "claim_basis": "Sources: two independent news reports and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0095", "catalog_version": "1.0.0", "source_fingerprint": "98c5275b8297241b39c99ee3714e3d5dfdb3a9236527256b7a4fc10d1caf9aed", "collection_refs": [ { "collection_id": "aibd", "version": "snapshot-8bf7cfec9cb185aa" }, { "collection_id": "aibd", "version": "2026-10-25" }, { "collection_id": "fructus", "version": "2026-10-25" }, { "collection_id": "viator", "version": "2026-10-25" }, { "collection_id": "adyt", "version": "2026-10-25" }, { "collection_id": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0095-source-0001", "AIBD-0095-source-0002", "AIBD-0095-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": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0095-legacy-value", "DIV-0017" ], "role": "Existing monetary observations and Fructus statements with matched AIBD case links; each statement retains its own review and eligibility status" }, "provenance": { "kind": "curatorial", "basis": "Linked to the current Fructus dataset by its validated case identifier. Eligible statements can supply unanswered monetary questions without a new source review or a finding of net benefit.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "record_ids": [ "AIBD-0095-legacy-value", "DIV-0017" ] }, "institutional decisions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "record_ids": [] } }, "fields": { "actors[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "developer": "Google (Flood Hub); trigger design by GiveDirectly with JBA Global Resilience", "deployer": "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 timing and geography: GiveDirectly told The New Humanitarian that satellite/AI flood modeling determined which communities were paid and when. Individual eligibility was decided by non-AI verification (ward residence, Smile ID checks, call-center calls). The investigation identifies that as the main source of exclusions. GiveDirectly's own page attributes the late payments to identity checks in two wards." } } }, "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": "protective response", "ai_role": { "status": "assessed", "statement": "Decisive for timing and geography: GiveDirectly told The New Humanitarian that satellite/AI flood modeling determined which communities were paid and when. Individual eligibility was decided by non-AI verification (ward residence, Smile ID checks, call-center calls). The investigation identifies that as the main source of exclusions. GiveDirectly's own page attributes the late payments to identity checks in two wards." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ai_role", "category" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].alternatives": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].assessor_date": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].depth": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "$105 to over 4,600 individuals, followed by a $210 transfer (recipient count not stated). Implementer survey reports weekly income doubling and fewer food shortages (self-reported, not independently verified)." }, { "group": "organizational", "depth": 1, "reason": "GiveDirectly gained a tested trigger pipeline and documented lessons. It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].display_confidence": { "state": "value_present", "value": { "kind": "structured", "data": { "confidence": "medium", "evidence_word": "reported", "claim_status": "Independently reported", "basis": "Sources: two independent news reports and the deployer's press release. The cited evidence includes no independent study or report and no finding by a regulator or court.", "interpretation": "Existing display rule preserved; the richer outcome-specific confidence question still needs review." } }, "provenance": { "kind": "curatorial", "basis": "Derived from the existing case display properties; not a new evidentiary judgment.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "assessments[].rationale": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "$105 to over 4,600 individuals, followed by a $210 transfer (recipient count not stated). Implementer survey reports weekly income doubling and fewer food shortages (self-reported, not independently verified)." }, { "group": "organizational", "depth": 1, "reason": "GiveDirectly gained a tested trigger pipeline and documented lessons. It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Recipients received cash, and GiveDirectly’s survey reports higher income and fewer food shortages. GiveDirectly also gained experience in sending payments after a forecast threshold was reached. It acknowledged that its recipient units were inconsistent. The investigation documents that some of the poorest residents were excluded." } }, "assessments[].rubric_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].status_basis": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "assessments[].target": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "depth": 2, "reason": "$105 to over 4,600 individuals, followed by a $210 transfer (recipient count not stated). Implementer survey reports weekly income doubling and fewer food shortages (self-reported, not independently verified)." }, { "group": "organizational", "depth": 1, "reason": "GiveDirectly gained a tested trigger pipeline and documented lessons. It acknowledged to The New Humanitarian that it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation was published on 31 August 2026." }, { "group": "societal", "depth": 2, "reason": "Material to a group of about 4,600 people in 53 communities. The investigation documents that some of the poorest were excluded, limiting wider benefit." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "availability": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "brief": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_kind": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "case_links[]": { "state": "value_present", "value": { "kind": "structured", "data": { "answer": "Google-built hazard alert system underestimated the earthquake and failed to warn residents; Flood Hub triggers from the same developer decided who received anticipatory cash.", "links": [ { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "group": "societal", "basis": "Google-built hazard alert system underestimated the earthquake and failed to warn residents; Flood Hub triggers from the same developer decided who received anticipatory cash.", "relation": "comparable_harm" } ] } }, "provenance": { "kind": "legacy_record", "basis": "Existing paired_incident classification; no new source review.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "claims[].evidence_links": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].evidence_strength": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].missingness": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "claims[].proposition": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "cross_border_flows[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "decisions[].action_outcome": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].grounds": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].institution_task": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "decisions[].success_criterion": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial", "family": "institutional decisions" }, "deployment_stage": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].amount": { "state": "value_present", "value": { "kind": "structured", "data": { "answer": "$105 (source equivalent: NGN177,866 per recipient). Reported equivalent; later naira payments may differ.", "monetary_statements": [ { "id": "DIV-0017", "title": "Flood-triggered cash transfers in Nigeria", "monetary_role": "recipient_transfer", "financial_type": "recipient_transfer", "amount_basis": "transfer", "net_benefit_established": false, "source_review_status": "primary_source_checked", "curation_status": "curated", "observations": [ { "id": "primary", "relationship": "primary", "amount": { "source_text": "US$105 per recipient", "value": "105", "qualifier": "exact", "currency": "USD", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "period": "August 29 to October 24, 2024, for the first transfer.", "unit": "Per recipient, first transfer.", "realization": "paid", "accounting_boundary": "Recipient cash transfer. A transfer is not a net increase in societal resources or an estimate of avoided flood loss.", "source_review_status": "primary_source_checked", "sources": [ { "url": "https://www.givedirectly.org/flood-forecast-ai", "title": "How AI helped send cash just before floods hit in Nigeria", "quote": "we sent $105 to over 4,600 individuals", "date": "2025-06-27", "primary": true, "written_by": "deployer", "reviewed_at": "2026-10-23", "review_method": "full_text" } ] }, { "id": "ngn-equivalent", "relationship": "source_equivalent", "equivalent_to": "primary", "amount": { "source_text": "NGN177,866 per recipient", "value": "177866", "qualifier": "exact", "currency": "NGN", "currency_basis": "explicit", "price_basis": "Nominal source amount; price year not separately stated." }, "period": "August 29 to October 24, 2024, for the first transfer.", "unit": "Per recipient, first transfer.", "realization": "paid", "accounting_boundary": "Recipient cash transfer. A transfer is not a net increase in societal resources or an estimate of avoided flood loss.", "source_review_status": "source_checked", "sources": [ { "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "title": "Could AI save Nigerians from devastating floods?", "quote": "177,866 naira ($105)", "date": "2024-09-21", "primary": false, "written_by": "independent", "reviewed_at": "2026-10-23", "review_method": "full_text", "locator": "First anticipatory payment in Kogi" } ] } ] } ], "original_resolution": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "value": { "amount": "$105", "what": "anticipatory cash transfer per recipient before the flood peak (a second $210 transfer followed)", "who": "over 4,600 flood-exposed residents of Ibaji, Kogi State", "estimate": false, "quote": "Once forecasted flood thresholds were met, we sent $105 to over 4,600 individuals before the flood peak.", "url": "https://www.givedirectly.org/flood-forecast-ai/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "value" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" } } }, "provenance": { "kind": "curatorial", "basis": "Mapped from eligible Fructus statements DIV-0017. Each statement retains its source review status. The mapping performs no new source review or currency conversion and does not establish additional value from AI. The Fructus audit matches the original monetary value. The original field and its provenance are retained with this answer.", "record_fields": [], "source_refs": [ "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "https://www.givedirectly.org/flood-forecast-ai" ] }, "review_state": "record_mapped", "coverage": "partial" }, "economics[].costs[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "economics[].counterfactual": { "state": "value_present", "value": { "kind": "structured", "data": { "answer": "The amount is not a comparison with aid delivered without AI.", "monetary_statements": [ { "id": "DIV-0017", "title": "Flood-triggered cash transfers in Nigeria", "monetary_role": "recipient_transfer", "financial_type": "recipient_transfer", "amount_basis": "transfer", "net_benefit_established": false, "source_review_status": "primary_source_checked", "curation_status": "curated", "observations": [ { "id": "primary", "relationship": "primary", "amount": { "source_text": "US$105 per recipient", "value": "105", "qualifier": "exact", "currency": "USD", "currency_basis": "context", "price_basis": "Nominal source amount; price year not separately stated." }, "period": "August 29 to October 24, 2024, for the first transfer.", "unit": "Per recipient, first transfer.", "realization": "paid", "accounting_boundary": "Recipient cash transfer. 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About 1,400 recipients were paid on 24 October 2024, after floods peaked on 21 October. A later US$210 transfer has no stated recipient count." } }, "external_references[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "paired_incident": { "database": "AI Incident Database", "identifier": "1546", "title": "Google Android Earthquake Alerts System Reportedly Underestimated Kahramanmaraş Earthquake and Failed to Send Urgent Warnings to Millions", "url": "https://incidentdatabase.ai/cite/1546", "group": "societal", "basis": "Google-built hazard alert system underestimated the earthquake and failed to warn residents; Flood Hub triggers from the same developer decided who received anticipatory cash." } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "paired_incident" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "follow_up[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "later": { "status": "scaled", "date": "2025-06-27", "note": "GiveDirectly's implementer report (published 27 June 2025, revised July 2026 to add the tradeoffs of remote phone-based targeting) says it is extending AI-triggered anticipatory cash with Google Research to new countries and other hazards.", "quote": "GiveDirectly continues to work with Google Research to scale the use of AI for anticipatory action to new countries and explore anticipatory cash transfers for other hazards, such as droughts.", "url": "https://www.givedirectly.org/flood-forecast-ai/" } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "later" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "human_oversight": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "id": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "id": "AIBD-0095" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "id" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "infrastructure_dependencies[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiaries": { "state": "value_present", "value": { "kind": "text", "text": "Residents of 53 communities in Ibaji, Kogi State, Nigeria." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "Residents of 53 communities in Ibaji, Kogi State, Nigeria." } }, "outcomes[].beneficiary_count": { "state": "value_present", "value": { "kind": "text", "text": "GiveDirectly reports more than 4,600 recipients. It acknowledged that it had used “household” and “individual” interchangeably." }, "provenance": { "kind": "legacy_record", "basis": "Moved the specific information already present in the entry into its applicable question. No new source review is implied.", "record_fields": [ "account", "ratings" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial", "display": { "answer": "GiveDirectly reports more than 4,600 recipients. It acknowledged that it had used “household” and “individual” interchangeably." } }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Risk", "taxonomy_note": "Cash paid before a forecast flood is read as reducing potential for harm; Trustible's description stresses legal harm." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type", "taxonomy_note" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "In 2024 GiveDirectly ran an anticipatory cash program in six wards of Ibaji, Kogi State, Nigeria. JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. 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https://www.givedirectly.org/flood-pilots/; https://web.archive.org/web/20260922092208/https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk 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": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-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://en.wikipedia.org/wiki/Google; 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https://www.givedirectly.org/flood-pilots/; https://web.archive.org/web/20260922092208/https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Risk 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": "Added what the benefit is measured against, what happened since, the value as stated from the cited sources.", "source": "https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-ai/; https://www.givedirectly.org/flood-forecast-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://en.wikipedia.org/wiki/Google; 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How AI helped send cash just before floods hit in Nigeria. GiveDirectly (implementer report; page updated 2026).", "title": "How AI helped send cash just before floods hit in Nigeria", "url": "https://www.givedirectly.org/flood-forecast-ai/", "date": "2025-06-27", "written_by": "deployer", "quote": "We sent early payments to 3,250 people between August 29 and October 6" }, { "kind": "news report", "citation": "Ayeni, D. (2026). GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria. 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Al Jazeera.", "title": "Could AI save Nigerians from devastating floods?", "url": "https://www.aljazeera.com/features/2024/9/21/could-ai-save-nigerians-from-devastating-floods", "date": "2024-09-21", "written_by": "independent", "quote": "Omali received his first transfer of 177,866 naira on August 31 this year." } ] } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "evidence" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "sources[].id": { "state": "value_present", "value": { "kind": "identifiers", "identifiers": [ "AIBD-0095-source-0001", "AIBD-0095-source-0002", "AIBD-0095-source-0003" ], "role": "Existing evidence items, in their original order" }, "provenance": { "kind": "curatorial", "basis": "Stable within the fingerprinted record version; no source content or source review has been added.", "record_fields": [], "source_refs": [] }, "review_state": "record_mapped", "coverage": "complete" }, "sources[].language": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "sources[].relationship": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "evidence": [ { "kind": "press release", "citation": "GiveDirectly (2025). How AI helped send cash just before floods hit in Nigeria. GiveDirectly (implementer report; page updated 2026).", "title": "How AI helped send cash just before floods hit in Nigeria", "url": "https://www.givedirectly.org/flood-forecast-ai/", "date": "2025-06-27", "written_by": "deployer", "quote": "We sent early payments to 3,250 people between August 29 and October 6" }, { "kind": "news report", "citation": "Ayeni, D. (2026). GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria. The New Humanitarian, investigation, 31 August 2026 (live page returns 403).", "title": "GiveDirectly's AI experiment left vulnerable flood victims behind in Nigeria", "url": "https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria", "date": "2026-08-31", "written_by": "independent", "quote": "Would-be aid recipients said they were excluded for submitting personal data that did not perfectly match government or bank records" }, { "kind": "news report", "citation": "Cheng, K.-C. (2024). Could AI save Nigerians from devastating floods? 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JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation appeared." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "summary", "account" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "systems[].autonomy_permissions": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].component_role": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].deployment_configuration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].inputs_outputs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_name": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].model_version": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "systems[].product_name": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "system": "Google Flood Hub AI river-flood forecasts accessed via API, with JBA Global Resilience hydrology analysis and a 20%-of-community-area trigger rule" } }, "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": "Google (Flood Hub); trigger design by GiveDirectly with JBA Global Resilience" } }, "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": "GiveDirectly paid anticipatory cash in Kogi State, Nigeria, on Google Flood Hub forecast triggers (August to October 2024)", "headline": "Google Flood Hub triggers pay $105 to 4,600 people in Kogi" } }, "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": [ "98c5275b8297241b39c99ee3714e3d5dfdb3a9236527256b7a4fc10d1caf9aed" ], "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": "In 2024 GiveDirectly ran an anticipatory cash program in six wards of Ibaji, Kogi State, Nigeria. JBA Global Resilience pulled Google Flood Hub forecasts by application programming interface (API), made inundation maps and set thresholds. GiveDirectly monitored forecasts daily and cross-checked them with community observations. When 20% of a community's area was forecast to flood the trigger was met and verified residents were paid. The New Humanitarian describes an automated payout command to verified residents. GiveDirectly reports $105 to over 4,600 individuals in 53 communities. 3,250 were paid between 29 August and 6 October 2024 and 1,400 on 24 October, after floods peaked on 21 October. A $210 transfer followed. Its own survey reports weekly incomes rising from ₦4,500 to ₦10,000 and food shortages falling from 97% to 7%. A New Humanitarian investigation (31 August 2026) found data mismatches and phone-access barriers excluded some of the most vulnerable residents. GiveDirectly told the outlet it had used “household” and “individual” interchangeably. Its page note says the page was updated in July 2026, before the investigation appeared.", "replication": { "produced_by": [ "Google Flood Hub forecasts via API", "20%-of-area trigger rule defined by GiveDirectly and JBA", "USSD pre-enrollment, Smile ID verification and call-center checks", "Cash transfers using recipients' self-reported bank details. GiveDirectly and the IRC received a $4.6 million Google.org grant in 2024 for AI-assisted anticipatory cash (The New Humanitarian). It is not stated to have funded the Kogi transfers" ], "must_hold": [ "Forecast skill on the Niger and Benue floodplain", "Phone access, charging and matching ID records among the poorest", "Funds released and verification completed before the hazard" ], "echoes": [ { "date": "2022-10-01", "setting": "GiveDirectly's 2022 Kogi State response: communities chosen before the season by layering historical flood data (Floodbase, Fathom), granular poverty maps and crop and village density derived from satellite imagery; people enrolled mid-September 2022 and paid once October 2022 flooding began (month only known; first day used)", "outcome": "unknown", "source": "https://www.givedirectly.org/flood-pilots/" }, { "setting": "GiveDirectly anticipatory programs in Kenya, Mozambique and a planned Bangladesh RCT (Jamuna basin)", "outcome": "unknown", "source": "The New Humanitarian (2026), https://www.thenewhumanitarian.org/investigations/2026/08/31/givedirectlys-ai-experiment-left-flood-victims-behind-nigeria" } ] } } }, "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", "display": { "answer": "GiveDirectly and JBA used Flood Hub forecasts to identify communities where at least 20% of the area was expected to flood. GiveDirectly verified residents’ identities and bank details before sending payments." } } }, "summary": { "total": 91, "addressed": 91, "known": 43, "reviewed": 0, "record_mapped": 43, "not_assessed": 48, "complete": false, "by_state": { "value_present": 43, "not_reported": 0, "unresolved": 0, "not_assessed": 48, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }