AI system
- System
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GiveDirectly MobileAid: machine-learning poverty targeting from mobile-phone metadata
- Model
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Not assessed
- Release
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Not assessed
- Application developer
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GiveDirectly (MobileAid), with research partners; for the Malawi study the Government of Malawi, U.C. Berkeley and U.C. San Diego - Role
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Not assessed
- Configuration
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Not assessed
- Inputs and outputs
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Not assessed
- Autonomy
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Not assessed
- Workflow
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Access to mobile network operator metadata for the target population under a data agreement. A machine-learning model relating phone usage patterns to poverty, trained on survey data. Mobile-money rails to enroll and pay identified people. An in-person survey component (65% of enrollments) alongside the phone-metadata component.
- Human oversight
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Not assessed
- Relation to AI
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Only the maker's account is among the sources. GiveDirectly says machine learning on phone metadata identified and enrolled 35% of recipients in a Malawi hybrid program. It says this halved the cost per additional enrolee. It gives no date, place, recipient count or accuracy for Malawi. The Government of Malawi, U.C. Berkeley and U.C. San Diego study is described as pending (findings expected in 2026). Likewise, the explain-AI page names no deployment, date or outcome.
- AI contribution
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Only the maker's account is among the sources. GiveDirectly says machine learning on phone metadata identified and enrolled 35% of recipients in a Malawi hybrid program. It says this halved the cost per additional enrolee. It gives no date, place, recipient count or accuracy for Malawi. The Government of Malawi, U.C. Berkeley and U.C. San Diego study is described as pending (findings expected in 2026). Likewise, the explain-AI page names no deployment, date or outcome.
Benefits
- Outcome identifiers
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Not assessed
- Benefit
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35% of recipients enrolled via phone data, cost per enrolee halved
- Category
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Improved Organizational Efficiency
- Status
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Not assessed
- Beneficiary groups
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People. Organizations. Society.
- Beneficiaries
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Not assessed
- Reach
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Not assessed
- Distribution and access
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Not assessed
- Duration
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Not assessed
- Expected?
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Yes
- Costs and harms
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Not assessed
Measurement
- Studies
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Not assessed
- Study design
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Not assessed
- Measurement
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Not assessed
- Setting and sample
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Not assessed
- Measures
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35% of recipients enrolled via phone data, cost per enrolee halved
- Comparison
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a fully in-person enrollment operation
- Results
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Result: cut the cost of identifying and enrolling each additional person in half.
- Additional value from AI
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Not assessed
- Limitations
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Not assessed
- Denominators
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Not assessed
- Materials for replication
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Not assessed
Money
- Kind of value
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Not assessed
- Amount
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Not assessed
- Currency and prices
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Not assessed
- Period and unit
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Not assessed
- Realization
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Not assessed
- Recipients and payers
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Not assessed
- Gross or net
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Not assessed
- Included costs
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Not assessed
- Comparison
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Not assessed
- Method
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Not assessed
- Return and payback
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Not assessed
People and places
- Participants
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Developer:
GiveDirectly (MobileAid), with research partners; for the Malawi study the Government of Malawi, U.C. Berkeley and U.C. San Diego. Operator: GiveDirectly, with the Government of Malawi for the urban social registry work. - Jurisdictions
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Benefit:
Malawi (the explain-AI post names Mzuzu). Developer: 
United States. Operator: United States. - Ownership and control
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Not assessed
- Dependencies
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Not assessed
- Cross-border connections
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Not assessed
- Geopolitical implications
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Not assessed
Follow-up
- Conditions for replication
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Telecom partnership and data-sharing agreement in place (GiveDirectly cites 6–18 months set-up). Phone ownership and usage among the poor population. Recipients' understanding and consent, which GiveDirectly found requires scenario-based explanation. Model accuracy acceptable for the program (GiveDirectly reports phone metadata below in-person proxy-means tests).
- Replication attempts
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3 follow-up entries. Benefit recurred: 2. Outcome unresolved: 1.
- Later findings
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GiveDirectly says a study with the Government of Malawi, U.C. Berkeley and U.C. San Diego is expected to report in 2026; nothing dated after 4 September 2025 was found in the cited sources.
- Availability
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Not assessed
- Institutional decision
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Not assessed
- Grounds
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Not assessed
- Action and result
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Not assessed
- Success criterion
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Not assessed
Evidence
- Source numbers
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AIBD-0351-source-0001–0004
- Citations
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Numbered references below.
- Language
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Not assessed
- Source relationships
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Developer
- Original evidence
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Not assessed
- Access and versions
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Not assessed
- Supported findings
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Not assessed
- Evidence links
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Not assessed
- Evidence strength
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Not assessed
- Missing evidence
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Not assessed
Grades
- Assessed benefits
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People. Organizations. Society.
- Depth
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Not assessed
- Reasons
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People: No dated event, named recipient or outcome for Malawi is given in any cited source. Organizations: The halved enrollment cost is the maker's own undated statement without figures or an independent evaluation. Society: No population-level outcome is documented for Malawi.
- Observed or intended
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Not assessed
- Rubric
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Not assessed
- Assessor and date
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Not assessed
- Alternative assessments
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Not assessed
- Fill
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The makers' claim
Entry details
- Kind
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Not assessed
- Related entries
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Not assessed
- Field
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Humanitarian cash transfers / poverty targeting. Non-governmental cash-transfer organization working with a national government.
- Purpose
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Identify people in poverty from mobile-phone metadata with machine learning so that cash can be delivered faster and more cheaply. The claimed benefit (cheaper, faster enrollment) is that purpose.
- Dates
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Date basis unassessed: 2025 (precision unassessed). Updated: 21 October 2026.
- Stage
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Not assessed
- Version
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SHA-256: 0f3426e3243f…
References
- Other identifiers
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Not assessed
- Category mappings
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Not assessed
- Brief
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Not assessed
- Revisions
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7 revisions. Latest: 21 October 2026.
Sources
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Press release Developer
Lummis, V. (2025). Five things we've learned using mobile data and AI/ML to identify people in need. GiveDirectly blog, 4 September 2025.
So in Malawi, we ran a hybrid program: we identified and enrolled 35% of recipients via phone metadata and the other 65% in person.
This combined approach still cut the cost of identifying and enrolling each additional person in half, compared to a fully in-person operation.
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Press release Developer
we interviewed 67 GiveDirectly recipients across Malawi and Kenya in February and October 2024
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Press release Developer
Lummis, V., Luk, S. & Ramprasad, S. (2024). GiveDirectly's approach to responsible AI/ML. GiveDirectly blog, 8 July 2024.
interviews with GiveDirectly recipients in Malawi and Kenya