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Mitigating AI gender bias in an international development context

Itad helped to mitigate AI gender bias in one of Mexico's leading education pilots - and provided important insights for the wider development sector to enable more equitable, inclusive, and transparent programming.


Each year, about 40,000 students drop out of the education system in Guanajuato, Mexico. To help address this urgent social challenge, the Mexican Government, in partnership with the World Bank via the Educational Trajectories initiative, created an AI-based early alert system aimed at improving school retention and graduation rates by identifying and then supporting at-risk students.

The pilot serves as a meaningful example of how government agencies are increasingly turning to AI to address pressing social challenges.

Fostering equitable and inclusive outcomes

Itad – in partnership with Women in Digital TransformationPIT Policy Lab, and Athena Infonomics – worked with the Mexican State of Guanajuato’s Ministry of Education to identify and mitigate gender bias within the pilot.

The consortium’s work was supported by a grant from USAID’s Equitable AI Challenge which seeks to foster an equitable and inclusive digital ecosystem by helping decision-makers address gender biases, harms, and inequitable outcomes resulting from AI technology.

Collaborating with the Secretariat of Education of the State of Guanajuato, Itad and partners reviewed anonymised data used to train the early alert system’s AI model. Leveraging IBM’s open-source

Itad and partners stressed the need to incorporate frameworks that went well beyond the Secretariat’s initial focus on privacy and person data protection. By facilitating a series of comprehensive workshops, the consortium strengthened Secretariat staff’s technical expertise in the ethical, responsible, and inclusive use of AI in the public sector.

Ethical guidance and AI checklist

To support knowledge building, the consortium developed an AI Ethics Guide and Checklist for policymakers and technical teams to drive ethical AI systems that are mindful of privacy and inclusivity.

The AI Ethics Guide presents a broad overview of what AI is, the ethical concerns it creates, and how they can be addressed at national, sub-national, and municipal levels. To illustrate ethics concerns, the guide presents several case studies and provocative questions that allow decision-makers to reflect on the responsible use of AI in government systems. To support knowledge building, the guide also includes a glossary of AI terminology derived from USAID learning studies and a comprehensive literature review with varied county approaches to using AI for public services.

The Checklist for AI Deployment is a separate yet interconnected tool for policymakers and technical teams preparing to deploy or already deploying AI systems. The document seeks to inform policymakers on starting points for building ethical AI systems as well as prompt technical experts to reflect on whether the right ethical guardrails are in place for an AI-based approach. Leading users through six phases, starting from regulatory foundations to the desired functionality of an AI system, the checklist contains questions on regulations, business processes, data collection and use, system design, and decision-making for ethical AI deployment in different situations and contexts.

Together these resources, with IBM’s AI Fairness 360 Toolkit, are helping to ensure policymakers in Guanajuato understand the potential for AI systems to reinforce existing biases, replicate privacy violations, or simply exclude populations.

Mike Klein, US Director of Itad, said:

“We hope that the resources developed through this project, benefit other development and humanitarian programmes, enabling them to mitigate bias in low- and-middle-income country datasets while ensuring their AI projects become more equitable, inclusive, and transparent.”

Promoting cross-country learning

Building on engagement in Mexico, Itad and partners shared implications of the work with other government representatives in Latin America. They also presented critical findings to state government leaders from Uttar Pradesh, Andhra Pradesh, Telangana, and Tamil Nadu in India — encouraging replicability of AI approaches in other regions while sharing lessons learned from the Mexico experience.