Projects / AI Innovation Challenge

Building an Organizational Gender Equality Assessment Functionality Using Machine Learning

Project completed!

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Equilo, a fast-growing startup with a mission to promote gender equality and social inclusion through smart technology, has teamed up with Omdena. The two-month Omdena Challenge brought together 50 AI experts to enhance Equilo’s existing app with a new organizational gender analysis feature. The AI-powered functionality suggests specific actions for organizations based on comparative data, including country-level and peer data. Equilo’s innovative solution provides a more cost-effective, faster, and customized approach to gender analysis and project management.

The problem

Gender equality is both a human right and a precondition for catalyzing development and lifting communities out of poverty. Empowered women contribute to healthy and productive families, communities, and nations. The UN recognizes the importance of gender equality in its Sustainable Development Goals.

Equilo is a web-based application that harnesses comprehensive global data to automate customized gender analysis for 132 countries and soon-to-be 18 sectors, from clean energy and agriculture to health and education. Equilo inspires individuals and organizations to participate and lead – empowering governments, aid agencies, and NGOs to make a difference in communities worldwide. The result is a far more cost-effective, faster route to insights and impactful action. Accelerating gender equality. Amplifying impact. Catalyzing change.

The project outcomes

The solution developed by the team is a new functionality for Equilo’s existing app, which includes an organizational gender analysis and intelligently automated suggested actions based on comparative data. The team gathered supplementary data from various sources to integrate into the primary data collected from surveys and used analytical algorithms to create a visualized analysis. The gathered data includes sources like Equality Check It, GlassDoor anonymous reviews, Behind the Brands,, and other anonymous/crowd-sourced data related to gender equality. The team also analyzed the strengths and gaps of organizations in relation to country-level data and peers. The result is a prioritized list of suggested actions automatically populated on the organization’s dashboard.

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Good English

A good/very good grasp in computer science and/or mathematics

Student, (aspiring) data scientist, (senior) ML engineer, data engineer, or domain expert (no need for AI expertise)

Programming experience with C/C++, C#, Java, Python, Javascript or similar

Experience in data analysis, web scraping, NLP, and/or web applications

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