This is a research prototype. The data and analyses are preliminary and not yet validated — we'd welcome your .
The Takaful cash transfer program in Jordan uses an opaque algorithm to rank families for financial assistance based on 57 socioeconomic indicators. Investigations by Human Rights Watch found that the system relies on inaccurate proxies for poverty, such as electricity consumption and car ownership, which fail to capture the economic reality of applicants. This has resulted in the exclusion of vulnerable families and the reinforcement of gender-based discrimination, prompting concerns about the transparency and fairness of algorithmic welfare distribution.
Takaful cash transfer program's algorithm which ranks families by their economic vulnerability level to determine financial assistance reportedly oversimplified people's economic situation, fueling social tension and perceptions of unfairness.
Domain classification, causal taxonomy, severity scores, and national security assessments were LLM-classified and may contain errors.
Unequal treatment of individuals or groups by AI, often based on race, gender, or other sensitive characteristics, resulting in unfair outcomes and unfair representation of those groups.
Also classified as
The algorithm fails to perform reliably or effectively as a poverty-targeting tool, relying on rigid and inaccurate proxies that exclude families in dire economic need.
The Takaful algorithm directly reinforces gender-based discrimination by utilizing sexist citizenship laws to calculate household sizes, resulting in unequal treatment and lower aid rankings for women married to non-citizens.
AI system
Due to a decision or action made by an AI system
Unintentional
Due to an unexpected outcome from pursuing a goal
Post-deployment
Occurring after the AI model has been trained and deployed
The exclusion of needy families and reinforcement of gender bias were unexpected and unintentional outcomes resulting from the deployment of the Takaful algorithmic ranking system.
Added categories peak at 3: Substantial (Psychological Harm, Inferred)
The deployment of Jordan's Takaful welfare ranking algorithm resulted in systemic exclusion of vulnerable families and reinforced gender-based discrimination. While causing notable social tension and distrust in public institutions, the incident represents a minor, localized national security impact manageable through standard administrative and policy reforms.
The Takaful system operates as a deterministic tool that processes 57 socioeconomic indicators to generate a poverty ranking for applicants without maintaining an internal state or exhibiting autonomous planning.
High Risk: The system is used by a public authority to determine eligibility for essential public assistance and social security benefits, which has significant implications for fundamental rights and public interests.
The primary purpose of the Takaful algorithm is to allocate financial assistance resources to households in Jordan by evaluating and ranking their applications.
The reports from Human Rights Watch and media outlets provide consistent, detailed accounts of the Takaful algorithm's deployment, its inputs, and its social impacts. While the exact mathematical weights of the 57 indicators remain secret, the qualitative impacts on applicants and the systemic biases (such as gender-based citizenship issues) are well-documented.
The government and World Bank frame the system as an objective, efficient tool to maximize constrained fiscal space, whereas human rights organizations view it as an arbitrary and discriminatory exclusion mechanism.
The specific list of the 57 indicators and their exact mathematical weights used in the ranking algorithm.
Technology
Rigid Algorithmic Formula
Formula flattens economic complexity, losing crucial nuance.
Inflexible Exclusion Thresholds
Automatic exclusion for asset ownership regardless of actual financial value.
Sexist Legal Code Integration
Sexist legal codes reduce household size calculation for certain women.
Data Inputs
Unreliable Proxy Indicators
Water and electricity usage are unreliable indicators of poverty.
Infrequent Data Collection
Infrequent surveys and registration lead to outdated applicant data.
Inaccurate Income Reporting
Applicants forced to alter financial data to bypass rigid system thresholds.
Human Factors
Overreliance on Automation
Overreliance on automated systems to solve complex social problems.
Applicant Confusion
Applicants lacked understanding of the secret ranking methodology and criteria.
Human Data Compilation Errors
Inaccuracies and inconsistencies introduced during manual data collection.
Process and Methods
Secret Ranking Methodology
Lack of transparency in the 57 indicators and how they are weighted.
Inadequate Validation Methods
Lack of validation to verify if indicators matched economic reality.
Flawed Expense Filtering
System rejected applications where expenses exceeded income by 20 percent.
Management
Prioritization of Fiscal Space
World Bank focused on targeting to restrict spending rather than universal aid.
Inadequate Risk Assessment
Management failed to assess risks of excluding vulnerable populations.
Lack of Accountability
NAF and World Bank avoided public disclosure and accountability for errors.
Regulatory Environment
Absence of Algorithmic Oversight
No regulatory framework to audit or challenge automated welfare decisions.
Lack of Transparency Mandates
No legal requirements forcing the disclosure of algorithmic weights.
Discriminatory Citizenship Laws
Sexist legal frameworks allowed to influence algorithm metrics.
AI system involved
Identified by automated extraction; can differ from the alleged developers above.
