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

SBIR 第一阶段：利用大数据、人工智能和机器学习进行性别平等和社会包容分析

• 批准号: 1843248
• 负责人: Jessica Menon
• 金额: $ 22.48万
• 依托单位: EQUILO INC.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2019-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1843248&HistoricalAwards=false
• 关键词: SBIR; Phase; Using; big; data

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.

这项小型企业创新研究（SBIR）I阶段项目的更广泛的影响/商业潜力是使经济发展援助组织能够计划，实施，衡量和实现更好的性别平等和社会包容成果。性别平等被认为是催化发展和使社区摆脱贫困的重要因素。赋予妇女有能力为健康和有效的家庭，社区和国家做出贡献。这样做的重要一步是将高质量和及时的性别分析整合在一起（检查性别角色，活动，需求和机会的差异如何在特定情况下影响男人，女人，男女），决策和项目计划。对于公共部门和私营部门，早期，面向行动的性别分析对于解决改善结果至关重要。 2015年至2016年，经合组织援助机构平均每年花费417亿美元，以性别平等和妇女赋权。保守地估计，这笔支出中有3-5％用于性别分析和行动计划，仅在发展领域，市场就达到了1.3-21亿美元的市场。该项目可以从该开发资金中改善结果。该小型企业创新研究（SBIR）I阶段项目是开发和测试基于Web的应用程序，该应用程序可以自动执行进行性别分析的当前手动过程。这解决了援助组织面临的现实世界挑战，以有效地将性别分析整合到计划，以告知有效及时的决策。尽管是许多组织的要求，但仍有采购和合同延迟，性别专家短缺以及手动研究和数据收集。 该项目将开发一个机器学习模型，该模型由专家创建的分析框架驱动，以自动化此过程并立即提供定制的结果，从而利用大数据，API和文本挖掘。第一阶段将建立，训练和测试一个部门和精选国家的模型。到第一阶段结束时，该模型将经过测试并迭代以符合高质量控制标准。一旦得到验证，它将准备在跨部门进行全球扩展。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评论标准来评估值得支持的。