{ "id": "AIBD-0031", "title": "Randomized evaluation of Google AI flood alerts with community dissemination in Bihar, India (2022–2023 monsoons)", "summary": "Bihar RCT, 319 panchayats: households relayed Google flood alerts reported 16.6% fewer illness symptoms and about 30% lower medical costs.", "date_observed": "2022-06-01", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Google's AI flood-forecasting system, run with India's Central Water Commission (CWC), issues riverine flood alerts as Android push notifications. In Bihar, India, economists Maulik Jagnani (Tufts) and Rohini Pande (Yale) ran a randomized evaluation across 319 panchayats in 12 flood-prone districts. It covered the 2022 and 2023 monsoon seasons (June to October). The non-governmental organization Yuganter trained community volunteers to relay the alerts. Treatment households got 2.41 more alerts in 2022 and were 46.5% more likely to get an alert before water reached their area. They reported 16.56% fewer illness symptoms and about 30% lower medical spending than controls. No specific evacuation tied to an alert is documented.", "category": "general benefit", "unexpected": false, "purpose": "The forecasting system was built to warn people of riverine floods before water arrives so they can prepare or evacuate. The measured preparedness and health effects are that intended benefit, but only at cohort level.", "system": "Google flood forecasting with Android push alerts (Google Public Alerts), plus community dissemination by Yuganter volunteers", "model_version": null, "developer": "Google", "deployer": "Central Water Commission of India with Google; last-mile dissemination by Yuganter (NGO) under the Jagnani-Pande evaluation", "location": "Bihar (twelve flood-prone districts), India", "domain": "Disaster risk reduction / flood early warning", "segment": "Rural households in riverine flood zones", "benefit_type": "Improved Physical Health", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "Effects are cohort averages across roughly 3.6 million people's communities (fewer illness symptoms, lower medical spending). No identifiable household with an attributable changed outcome is documented." }, { "group": "societal", "depth": null, "reason": "The study estimates population-level preparedness and health effects from a randomized design. It reports no discrete event such as a named evacuation." } ], "ai_role": { "status": "assessed", "statement": "The AI forecast generated the alerts. The randomized contrast, however, is between community dissemination of those alerts and alerts alone. So the measured effect is attributable to the dissemination of AI forecasts rather than to forecast accuracy itself. A 2019 pilot in which the only added treatment was raising alert awareness with a single local leader found no treatment impact." }, "evidence": [ { "kind": "preprint", "citation": "Jagnani M, Pande R (2024). Forecasting Fate: Experimental Evaluation of a Flood Early Warning System. Working paper, Tufts University and Yale University (Economic Growth Center), 3 April 2024.", "title": "Forecasting Fate: Experimental Evaluation of a Flood Early Warning System", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "date": "2024-04-03", "written_by": "independent", "quote": "treatment households exhibiting higher adaptation and health scores and a 30% reduction in medical costs compared to control communities" }, { "kind": "report", "citation": "Abdul Latif Jameel Poverty Action Lab (J-PAL) (n.d.). Disseminating Flood Warnings to Increase Disaster Preparedness in India. Evaluation summary (PDF).", "title": "Disseminating Flood Warnings to Increase Disaster Preparedness in India", "url": "https://www.povertyactionlab.org/print/pdf/node/8026750", "date": null, "written_by": "independent", "quote": "households in treatment communities received 2.37 more alerts (a 290 percent increase) on average during the 2022 flood season (June-October)" } ], "replication": { "produced_by": [ "Google/CWC flood alerts available to all 319 study panchayats (both arms); the randomized element was volunteer dissemination", "Trained community volunteers relaying smartphone alerts in villages with low literacy and limited location-services use", "Repeated flood seasons (2022 was more intense than 2023) allowing effects to be measured" ], "must_hold": [ "Alerts reach households before water arrives (46.5% more likely in treatment communities in 2022)", "Households trust alerts despite more false positives (8.09% vs 3.64%)", "Dissemination capacity is maintained. From 2023 two volunteers per panchayat replaced three, supplemented by direct WhatsApp/SMS messages to treatment households enrolled in the 2022 endline survey." ], "echoes": [ { "date": "2023-06-01", "setting": "2023 monsoon season in the same 319 Bihar panchayats: treatment households received 4.35 more alerts and were 88% more likely to receive an alert before water reached their panchayat", "model_version": null, "outcome": "recurred", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" } ] }, "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": "Same developer and channel: Google's Android hazard alerts; flood alerts cut illness in Bihar, while the Android Earthquake Alerts system underestimated the Kahramanmaraş quake and failed to warn millions of Android users in Turkey." }, "headline": "Google flood alerts in Bihar cut reported illness by 16.6%", "metric": "16.56% fewer illness symptoms and about 30% lower medical expenditure", "comparator": { "against": "control communities, where households had access to the smartphone alerts but no community volunteers relaying them", "before": "control households (reference group)", "after": "0.36 fewer illness symptoms (a 16.56% decrease); roughly INR 6,500 less on illness treatment (a 32.87% decrease)", "basis": "measured", "quote": "Overall, treatment households observed 0.36 fewer illness symptoms (a 16.56% decrease) compared to control households (Table 4). As a result, households in treatment areas incurred roughly INR 6,500 less on illness treatment (a 32.87% decrease).", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" }, "later": { "status": "unknown", "date": null, "note": "Neither cited source reports a development after the 2022-2023 evaluation seasons.", "quote": null, "url": null }, "value": { "amount": "INR 6,500", "what": "less spent on illness treatment per household over the flood season", "who": "treatment households in the Bihar study panchayats", "estimate": false, "quote": "As a result, households in treatment areas incurred roughly INR 6,500 less on illness treatment (a 32.87% decrease). In fact, based on the estimated reduction in medical expenditures alone, we estimate that for every $1 spent on the program, the intervention would generate a minimum benefit of $30 during a severe flooding season.", "url": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" }, "developer_country": "United States", "deployer_country": "India", "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 (high confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1546", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Removed “Flood Hub”, “Google Research” and the Kosi-river claim because neither cited source states them, and reworded the 2023 volunteer change and the 2019 pilot to match the working paper, which adds direct WhatsApp/SMS messaging in 2023 and describes the pilot's treatment as amplifying alert awareness to a single local leader.", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Improved Physical Health 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, the value as stated from the cited sources.", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf; https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf", "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://en.wikipedia.org/wiki/Central_Water_Commission", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, purpose, the replication conditions, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "studied", "shade": "high", "claim_status": "Independently studied", "claim_basis": "Sources: one independent report and one independent preprint. The cited evidence includes no finding by a regulator or court.", "profile": { "case_id": "AIBD-0031", "catalog_version": "1.0.0", "source_fingerprint": "1bfab83fdd45a0a55bd8861c4524bc8554fc9c682ab1f94bcf4c48375513c3f8", "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": "chanticleer", "version": "2026-10-25" } ], "source_ids": [ "AIBD-0031-source-0001", "AIBD-0031-source-0002" ], "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-0031-legacy-value", "DIV-0010" ], "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. 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In Bihar, India, economists Maulik Jagnani (Tufts) and Rohini Pande (Yale) ran a randomized evaluation across 319 panchayats in 12 flood-prone districts. It covered the 2022 and 2023 monsoon seasons (June to October). The non-governmental organization Yuganter trained community volunteers to relay the alerts. Treatment households got 2.41 more alerts in 2022 and were 46.5% more likely to get an alert before water reached their area. They reported 16.56% fewer illness symptoms and about 30% lower medical spending than controls. No specific evacuation tied to an alert is documented." } }, "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 forecasting with Android push alerts (Google Public Alerts), plus community dissemination by Yuganter volunteers" } }, "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" } }, "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": "Randomized evaluation of Google AI flood alerts with community dissemination in Bihar, India (2022–2023 monsoons)", "headline": "Google flood alerts in Bihar cut reported illness by 16.6%" } }, "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": [ "1bfab83fdd45a0a55bd8861c4524bc8554fc9c682ab1f94bcf4c48375513c3f8" ], "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": "Google's AI flood-forecasting system, run with India's Central Water Commission (CWC), issues riverine flood alerts as Android push notifications. In Bihar, India, economists Maulik Jagnani (Tufts) and Rohini Pande (Yale) ran a randomized evaluation across 319 panchayats in 12 flood-prone districts. It covered the 2022 and 2023 monsoon seasons (June to October). The non-governmental organization Yuganter trained community volunteers to relay the alerts. Treatment households got 2.41 more alerts in 2022 and were 46.5% more likely to get an alert before water reached their area. They reported 16.56% fewer illness symptoms and about 30% lower medical spending than controls. No specific evacuation tied to an alert is documented.", "replication": { "produced_by": [ "Google/CWC flood alerts available to all 319 study panchayats (both arms); the randomized element was volunteer dissemination", "Trained community volunteers relaying smartphone alerts in villages with low literacy and limited location-services use", "Repeated flood seasons (2022 was more intense than 2023) allowing effects to be measured" ], "must_hold": [ "Alerts reach households before water arrives (46.5% more likely in treatment communities in 2022)", "Households trust alerts despite more false positives (8.09% vs 3.64%)", "Dissemination capacity is maintained. From 2023 two volunteers per panchayat replaced three, supplemented by direct WhatsApp/SMS messages to treatment households enrolled in the 2022 endline survey." ], "echoes": [ { "date": "2023-06-01", "setting": "2023 monsoon season in the same 319 Bihar panchayats: treatment households received 4.35 more alerts and were 88% more likely to receive an alert before water reached their panchayat", "outcome": "recurred", "source": "https://bpb-us-w2.wpmucdn.com/campuspress.yale.edu/dist/7/2986/files/2024/04/Google_Floods-bcdf49042e23059a.pdf" } ] } } }, "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": 38, "reviewed": 0, "record_mapped": 38, "not_assessed": 53, "complete": false, "by_state": { "value_present": 38, "not_reported": 0, "unresolved": 0, "not_assessed": 53, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }