{ "id": "AIBD-0097", "title": "American Red Cross used Facebook's machine-learning population maps to focus Malawi mapping for the June 2017 measles vaccination campaign (Measles & Rubella Initiative)", "summary": "Facebook's ML settlement maps cut Red Cross mapping for Malawi's June 2017 measles vaccination campaign (Measles & Rubella Initiative) from 94,080 km² to 2,745 km²", "date_observed": "2017-06-07", "date_basis": "unassessed", "date_precision": "unassessed", "benefit_status": null, "source_collection": null, "source_entry": null, "account": "Malawi's June 2017 measles vaccination campaign (Measles & Rubella Initiative) targeted more than 7.5 million children. The American Red Cross's Missing Maps team needed base maps for campaign monitoring. Facebook's population maps come from a machine-learning computer-vision method that detects buildings in satellite imagery. They were used to identify where people lived and which areas were already mapped in OpenStreetMap. Concentrating volunteers on settlements filtered out 97.1% of the land area. That reduced the area to map from 94,080 km² to 2,745 km² and completed the base map in a fraction of the normal time. The base map was to feed a dashboard for government and Red Cross monitors during the campaign. The article predates the campaign and reports no monitoring outcome. No vaccination-coverage outcome attributable to the maps is reported.", "category": "benefit created", "unexpected": false, "purpose": "Produce high-resolution population and settlement maps from satellite imagery for planning and humanitarian use.", "system": "Facebook (Meta) High Resolution Settlement Layer population density maps", "model_version": null, "developer": "Facebook Inc. with CIESIN, Columbia University, and the World Bank Development Data Group", "deployer": "American Red Cross (Missing Maps) supporting the Measles and Rubella Initiative campaign in Malawi", "location": "Malawi (nationwide)", "domain": "Humanitarian mapping for immunization campaign support", "segment": "Humanitarian NGO", "benefit_type": "Reduced Task Time", "taxonomy_note": null, "registry_ids": {}, "ratings": [ { "group": "personal", "depth": null, "reason": "The source reports no outcome for the 7.5 million targeted children. Any benefit is indirect and unquantified." }, { "group": "organizational", "depth": 2, "reason": "Mapping area reduced from 94,080 km² to 2,745 km² and the base map completed in a fraction of the normal time." }, { "group": "societal", "depth": null, "reason": "A supporting role in one national campaign whose coverage effect was not measured." } ], "ai_role": { "status": "assessed", "statement": "The Facebook settlement data determined where volunteer mapping effort went and which areas were skipped. The campaign itself was a Measles & Rubella Initiative effort supported by the American Red Cross and other partners, with monitors from the national government. The Red Cross attributes efficiency, not coverage, to the maps. The Red Cross account is the implementing organization's and names only “Facebook's population datasets”, without mentioning machine learning. The identification with the High Resolution Settlement Layer and the machine-learning description are taken from the vendor-authored Tiecke et al. preprint. It does not mention the Malawi campaign." }, "evidence": [ { "kind": "press release", "citation": "American Red Cross (2017). Data: A vital tool for disaster response.", "title": "Data: A vital tool for disaster response", "url": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html", "date": "2017-06-07", "written_by": "deployer", "quote": "This technique reduced our mapping area from 94,080km2 to 2,745 km2." }, { "kind": "preprint", "citation": "Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., et al. (2017). Mapping the world population one building at a time. arXiv:1712.05839.", "title": "Mapping the world population one building at a time", "url": "https://arxiv.org/abs/1712.05839", "date": "2017-12-15", "written_by": "makers", "quote": "a computer vision method based on machine learning to create population maps from satellite imagery at a global scale" }, { "kind": "press release", "citation": "Eros, E., Schmeltzer, A. (2017). Stop the spots: Measles vaccinations in Malawi. Missing Maps blog (American Red Cross), 10 August 2017.", "title": "Stop the spots: Measles vaccinations in Malawi", "url": "https://www.missingmaps.org/blog/2017/08/10/malawi/", "date": "2017-08-10", "written_by": "deployer", "quote": null } ], "replication": { "produced_by": [ "Facebook settlement detection from satellite imagery", "Missing Maps volunteer tracing into OpenStreetMap", "Comparison with existing OpenStreetMap coverage", "Mobile data-collection dashboard for campaign monitors" ], "must_hold": [ "Imagery recent enough to reflect current settlements", "Building-detection accuracy in rural Malawi", "Volunteer and local mapping capacity" ], "echoes": [] }, "paired_incident": { "database": "AI Incident Database", "identifier": "1159", "title": "Government‑Backed AI4Peat Mapping Tool Allegedly Misidentifies Granite Outcrops and Quarries as Peat", "url": "https://incidentdatabase.ai/cite/1159", "severity": null, "group": "organizational", "basis": "Government-backed machine-learning mapping from imagery misclassified land as peat, misleading restoration programs; Facebook's building-detection population maps steer humanitarian mapping the same way." }, "headline": "Facebook ML maps cut Red Cross Malawi mapping area to 2,745 km²", "metric": "reducing the area to map from 94,080 km² to 2,745 km²", "comparator": { "against": "mapping the whole national land area without the settlement maps (the normal Missing Maps approach)", "before": "94,080km2", "after": "2,745 km2", "basis": "measured", "quote": "Much of Malawi is rural, so concentrating on population settlements allowed us to filter out 97.1% of the total land area that we sought to map. This technique reduced our mapping area from 94,080km2 to 2,745 km2. Volunteers could then spend their time mapping communities instead of searching through acres of uninhabited areas; they were able to complete the mapping in a fraction of the normal time.", "url": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html" }, "later": { "status": "persisted", "date": "2017-08-10", "note": "After the June 2017 campaign the Red Cross's Missing Maps team reported that the traced base map underpinned a real-time monitoring dashboard covering 13,743 children, and again credited Facebook's population data with filtering uninhabited areas out of the tracing tasks.", "quote": "We are also grateful for Facebook’s high-resolution population data for Malawi, which helped us to identify areas with and without concentrations of people. This made the tracing much, much faster – we filtered uninhabited areas out of the tracing tasks so that we could direct remote mappers towards areas that had features for them to map.", "url": "https://www.missingmaps.org/blog/2017/08/10/malawi/" }, "value": null, "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 1159 (medium confidence); severity not given by the source.", "source": "https://incidentdatabase.ai/cite/1159", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Recast the monitoring dashboard as planned use because the 7 June 2017 article predates the June campaign (and dropped it from the organisational rating reason), replaced the unsourced Ministry of Health with the Measles & Rubella Initiative partners and the national government, changed “measles-rubella campaign” to the source's “measles vaccination campaign (Measles & Rubella Initiative)” in title, summary and account, corrected the developer to the preprint's affiliations (Facebook Inc., CIESIN at Columbia University, World Bank Development Data Group) with a note that the Red Cross names only “Facebook's population datasets”, and reclassified the Red Cross news post as “other”.", "source": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html (Wayback snapshot); https://arxiv.org/abs/1712.05839", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Benefit type set to Reduced Task Time under Trustible's full taxonomy of 23 types; it was Improved Organizational Efficiency 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 from the cited sources.", "source": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html; https://www.missingmaps.org/blog/2017/08/10/malawi/", "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/Meta_Platforms; https://en.wikipedia.org/wiki/American_Red_Cross", "editor": "AIBD" }, { "date": "2026-10-21", "change": "Prose pass: a rating reason, account, the system's part rewritten in shorter sentences with abbreviations expanded; facts, figures and quotations unchanged.", "source": null, "editor": "AIBD" } ], "confidence": "claimed", "shade": "low", "claim_status": "The deployer's account", "claim_basis": "Sources: the makers' preprint and two press releases from the deployer. The cited evidence includes no independent source and no finding by a regulator or court.", "profile": { "case_id": "AIBD-0097", "catalog_version": "1.0.0", "source_fingerprint": "2e41991900b4e6881e7934ca89d0c360f5dae1a3f689ee5b484c9f91a82c83cd", "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-0097-source-0001", "AIBD-0097-source-0002", "AIBD-0097-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": "Facebook Inc. with CIESIN, Columbia University, and the World Bank Development Data Group", "deployer": "American Red Cross (Missing Maps) supporting the Measles and Rubella Initiative campaign in Malawi" } }, "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": "The Facebook settlement data determined where volunteer mapping effort went and which areas were skipped. The campaign itself was a Measles & Rubella Initiative effort supported by the American Red Cross and other partners, with monitors from the national government. The Red Cross attributes efficiency, not coverage, to the maps. The Red Cross account is the implementing organization's and names only “Facebook's population datasets”, without mentioning machine learning. The identification with the High Resolution Settlement Layer and the machine-learning description are taken from the vendor-authored Tiecke et al. preprint. 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The identification with the High Resolution Settlement Layer and the machine-learning description are taken from the vendor-authored Tiecke et al. preprint. It does not mention the Malawi campaign." } } }, "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", "reason": "The source reports no outcome for the 7.5 million targeted children. 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"organizational", "basis": "Government-backed machine-learning mapping from imagery misclassified land as peat, misleading restoration programs; Facebook's building-detection population maps steer humanitarian mapping the same way." } } }, "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": "persisted", "date": "2017-08-10", "note": "After the June 2017 campaign the Red Cross's Missing Maps team reported that the traced base map underpinned a real-time monitoring dashboard covering 13,743 children, and again credited Facebook's population data with filtering uninhabited areas out of the tracing tasks.", "quote": "We are also grateful for Facebook’s high-resolution population data for Malawi, which helped us to identify areas with and without concentrations of people. This made the tracing much, much faster – we filtered uninhabited areas out of the tracing tasks so that we could direct remote mappers towards areas that had features for them to map.", "url": "https://www.missingmaps.org/blog/2017/08/10/malawi/" } } }, "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-0097" } }, "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": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].beneficiary_count": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].benefit_type": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "benefit_type": "Reduced Task Time" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].claim": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "account": "Malawi's June 2017 measles vaccination campaign (Measles & Rubella Initiative) targeted more than 7.5 million children. The American Red Cross's Missing Maps team needed base maps for campaign monitoring. Facebook's population maps come from a machine-learning computer-vision method that detects buildings in satellite imagery. They were used to identify where people lived and which areas were already mapped in OpenStreetMap. Concentrating volunteers on settlements filtered out 97.1% of the land area. That reduced the area to map from 94,080 km² to 2,745 km² and completed the base map in a fraction of the normal time. The base map was to feed a dashboard for government and Red Cross monitors during the campaign. The article predates the campaign and reports no monitoring outcome. No vaccination-coverage outcome attributable to the maps is reported.", "benefit_type": "Reduced Task Time" } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "account", "benefit_type" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "outcomes[].distribution": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].duration": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].id": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].stakeholder_group": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "ratings": [ { "group": "personal", "reason": "The source reports no outcome for the 7.5 million targeted children. Any benefit is indirect and unquantified." }, { "group": "organizational", "depth": 2, "reason": "Mapping area reduced from 94,080 km² to 2,745 km² and the base map completed in a fraction of the normal time." }, { "group": "societal", "reason": "A supporting role in one national campaign whose coverage effect was not measured." } ] } }, "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" }, "outcomes[].status": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].tradeoffs": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "outcomes[].unexpected": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "unexpected": false, "purpose": "Produce high-resolution population and settlement maps from satellite imagery for planning and humanitarian use." } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; 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external sources were not rechecked.", "record_fields": [ "date_observed", "revisions" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "replication.attempts[]": { "state": "not_assessed", "review_state": "not_assessed", "coverage": "partial" }, "replication.conditions": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "replication": { "produced_by": [ "Facebook settlement detection from satellite imagery", "Missing Maps volunteer tracing into OpenStreetMap", "Comparison with existing OpenStreetMap coverage", "Mobile data-collection dashboard for campaign monitors" ], "must_hold": [ "Imagery recent enough to reflect current settlements", "Building-detection accuracy in rural Malawi", "Volunteer and local mapping capacity" ], "echoes": [] } } }, "provenance": { "kind": "legacy_record", "basis": "Preserved from the existing AIBD record; external sources were not rechecked.", "record_fields": [ "replication" ], "source_refs": [] }, "review_state": "record_mapped", "coverage": "partial" }, "revision_history[]": { "state": "value_present", "value": { "kind": "record_fields", "fields": { "revisions": [ { "date": "2026-10-20", "change": "Record drafted from the cited sources.", "editor": "AIBD" }, { "date": "2026-10-20", "change": "Paired with AI Incident Database incident 1159 (medium confidence); 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Data: A vital tool for disaster response.", "title": "Data: A vital tool for disaster response", "url": "https://www.redcross.org/about-us/news-and-events/news/Data-A-vital-tool-for-disaster-response.html", "date": "2017-06-07", "written_by": "deployer", "quote": "This technique reduced our mapping area from 94,080km2 to 2,745 km2." }, { "kind": "preprint", "citation": "Tiecke, T. G., Liu, X., Zhang, A., Gros, A., Li, N., Yetman, G., et al. (2017). Mapping the world population one building at a time. arXiv:1712.05839.", "title": "Mapping the world population one building at a time", "url": "https://arxiv.org/abs/1712.05839", "date": "2017-12-15", "written_by": "makers", "quote": "a computer vision method based on machine learning to create population maps from satellite imagery at a global scale" }, { "kind": "press release", "citation": "Eros, E., Schmeltzer, A. (2017). Stop the spots: Measles vaccinations in Malawi. 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The American Red Cross's Missing Maps team needed base maps for campaign monitoring. Facebook's population maps come from a machine-learning computer-vision method that detects buildings in satellite imagery. They were used to identify where people lived and which areas were already mapped in OpenStreetMap. Concentrating volunteers on settlements filtered out 97.1% of the land area. That reduced the area to map from 94,080 km² to 2,745 km² and completed the base map in a fraction of the normal time. The base map was to feed a dashboard for government and Red Cross monitors during the campaign. The article predates the campaign and reports no monitoring outcome. No vaccination-coverage outcome attributable to the maps is reported." } }, "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": "Facebook (Meta) High Resolution Settlement Layer population density maps" } }, "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": "Facebook Inc. with CIESIN, Columbia University, and the World Bank Development Data Group" } }, "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": "American Red Cross used Facebook's machine-learning population maps to focus Malawi mapping for the June 2017 measles vaccination campaign (Measles & Rubella Initiative)", "headline": "Facebook ML maps cut Red Cross Malawi mapping area to 2,745 km²" } }, "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": [ "2e41991900b4e6881e7934ca89d0c360f5dae1a3f689ee5b484c9f91a82c83cd" ], "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": "Malawi's June 2017 measles vaccination campaign (Measles & Rubella Initiative) targeted more than 7.5 million children. The American Red Cross's Missing Maps team needed base maps for campaign monitoring. Facebook's population maps come from a machine-learning computer-vision method that detects buildings in satellite imagery. They were used to identify where people lived and which areas were already mapped in OpenStreetMap. Concentrating volunteers on settlements filtered out 97.1% of the land area. That reduced the area to map from 94,080 km² to 2,745 km² and completed the base map in a fraction of the normal time. The base map was to feed a dashboard for government and Red Cross monitors during the campaign. The article predates the campaign and reports no monitoring outcome. No vaccination-coverage outcome attributable to the maps is reported.", "replication": { "produced_by": [ "Facebook settlement detection from satellite imagery", "Missing Maps volunteer tracing into OpenStreetMap", "Comparison with existing OpenStreetMap coverage", "Mobile data-collection dashboard for campaign monitors" ], "must_hold": [ "Imagery recent enough to reflect current settlements", "Building-detection accuracy in rural Malawi", "Volunteer and local mapping capacity" ], "echoes": [] } } }, "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": 30, "reviewed": 0, "record_mapped": 30, "not_assessed": 61, "complete": false, "by_state": { "value_present": 30, "not_reported": 0, "unresolved": 0, "not_assessed": 61, "not_applicable": 0, "withheld": 0, "source_unavailable": 0 } } } }