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SBIR Phase I: Using big data, AI, and machine learning in gender equality and social inclusion analysis

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 1843248
Agency Tracking Number: 1843248
Amount: $224,814.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: IT
Solicitation Number: N/A
Solicitation Year: 2018
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-02-01
Award End Date (Contract End Date): 2019-07-31
Small Business Information
1780 E 1338 RD, LAWRENCE, KS, 66044
DUNS: 081124825
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: Y
Principal Investigator
 Jessica Menon
 (312) 468-0170
Business Contact
 Jessica Menon
Phone: (312) 468-0170
Research Institution
The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable economic development aid organizations to plan, implement, measure, and achieve better gender equality and social inclusion outcomes. Gender equality is recognized as an important factor in catalyzing development and lifting communities out of poverty. Empowered women contribute to healthy and productive families, communities, and nations. An important step in doing this is to integrate high quality and timely gender analysis (examination of how differences in gender roles, activities, needs, and opportunities affect men, women, girls and boys in specific contexts), into decision-making and project planning. For both public and private sectors, early, action-oriented gender analysis is vital to tackle improve outcomes. In 2015 - 2016, OECD aid agencies spent, on average, $41.7 billion per year targeting gender equality and women's empowerment. Conservatively estimating that 3-5% of this expenditure is for gender analysis and action planning, that makes a market of $1.3-2.1 billion in the development sector alone. This project can potentially improve outcomes from that development funding. This Small Business Innovation Research (SBIR) Phase I project is to develop and test a web-based application that automates the currently manual process of conducting gender analyses. This addresses a real world challenge faced by aid organizations to effectively integrate gender analyses into planning to inform effective and timely decision-making. Despite being a requirement by many organizations, there are procurement and contract delays, shortage of gender experts, and manual research and data collection. This project will develop a machine learning model that leverages big data, APIs, and text-mining, driven by an analytic framework created by experts, to automate this process and deliver customized results instantly for a fraction of the cost. Phase I will build, train, and test the model for one sector and select countries. By the end of Phase I, the model will be tested and iterated to meet high quality control standards. Once validated, it will be ready to scale globally across sectors. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

* Information listed above is at the time of submission. *

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