AymurAI: Structuring Judicial Data on Gender-Based Violence

AymurAI: Structuring Judicial Data on Gender-Based Violence

Argentina flag

Argentina

Public Sector

High replicability and adaptation

Implementing Organisation

DataGénero

Argentina, Argentina, Buenos Aires

Deployed across Argentina, Chile, and Costa Rica

Civil Society

Implementing Point of Contact

Vana Feldfeber

Author / Researcher, DataGénero

Contributor of the Impact Story

UN Women

Year of implementation

2022

Problem statement

In Latin America, knowledge about gender-based violence is trapped in unstructured judicial documents, accessible only through slow manual processes. Weak transparency and accountability in judicial institutions contribute to public distrust. Data systems focusing narrowly on feminicide obscure the broader spectrum of gender-based violence. The absence of traceable, high-quality data on GBV is a pressing structural issue that limits prevention and oversight.

Submission Overview

DataGénero is a feminist data organization in Argentina that develops open-source AI tools grounded in feminist epistemologies. AymurAI was developed in 2022 in collaboration with Criminal Court 10 of Buenos Aires, academic researchers and private consulting firms, funded by philanthropists and international organizations.

AI Technology Used

Natural Language Processing

Named Entity Recognition for anonymization (detecting 30 types of sensitive information), Open-Source Desktop Architecture

Key Outcomes

Efficiency & Productivity

Accuracy & Quality Improvement

Inclusion & Equity

Knowledge & Skills Impact

AymurAI is an open-source desktop application using NLP to anonymize and structure judicial rulings on gender-based violence across Latin America. Deployed in 200+ institutions across Argentina, Chile and Costa Rica, it has created a repository of over 10,000 anonymized criminal court records. The system found that psychological and symbolic violence are the most prevalent forms (28%), physical violence appears in 20% of cases, while sexual, economic, social and environmental violence appear less frequently - making invisible patterns of GBV visible for institutional accountability and evidence-based policymaking.

Impact Metrics

Scale of AymurAI deployment across Institutions

Baseline Value

NA

Post-Implementation

Stable deployment across 200+ institutions in Argentina, Chile and Costa Rica has been recorded

Anonymized Court Records Created using AymurAI

Baseline Value

NA

Post-Implementation

Repository of over 10,000 anonymized criminal court records on gender-based violence from Latin America has been documented

Types of Gender Based Violence Documented on AymurAI

Baseline Value

NA

Post-Implementation

Psychological/symbolic violence most prevalent (28%), physical violence (20%), with sexual, economic, social and environmental violence have also been captured

Court Officials Served through AymurAI

Baseline Value

NA

Post-Implementation

Approximately 2,000 paralegal employees across three countries use the system.

Implementation Context

Deployed

Deployed in more than 200 judicial institutions across Argentina, Chile and Costa Rica

2,000 paralegal employees/court officials, women, girls and LGBTIQ+ individuals affected by gender-based violence across the justice system

Key Partnerships

Criminal Court 10 of Buenos Aires (pilot partner), academic researchers, UN Women, philanthropic organizations

Replicability & Adaptation

High

1. Frame AI as public-interest infrastructure, not standalone product 2. Adapt NLP models to local languages and legal terminology 3. Build long-term relationships with courts for sustained adoption 4. Address judicial inertia and resistance to new data responsibilities 5. Maintain consistent ethical standards across regions

Open-source architecture enables replication. Minimal infrastructure requirements - no internet access required, operates on local court servers or personal computers. Judicial documents remain on host computer, reducing security breach exposure.

* The data presented is self-reported by the respective organisations. Readers should consult the original sources for further details.