Artificial intelligence (AI) has emerged as a transformative force across numerous industries, including the nonprofit sector. AI encompasses various technologies designed to perform tasks that traditionally require human intelligence, such as understanding language, analyzing data, and making recommendations.

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This set of guidelines covers five focus areas that can help organizations evaluate generative AI’s risks and considerations as these tools gain mainstream adoption. These guidelines don’t replace principles but instead act as a North Star for how generative AI can be operationalized and put into practice as organizations develop products and services that use this new technology.

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To ensure the most trustworthy and responsible data-driven decisions are made across organizations, this resource provides a central repository of curated, reviewed, and well-organized best practices for various data problems. These problems are both commonly encountered across organizations and are critical to a trustworthy use of data. This resource empowers people working with data to follow approved techniques to solve important products and problems.

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In order to understand the reality on the ground, this resource provides three mini-briefs based on initiatives in developing countries that seek to reach and provide highly localized digital products and services for digitally marginalized populations through a human-centered approach to data governance. 

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As people have used or heard about the power of OpenAI’s GPT-4 foundation model, they have often been surprised or even astounded. Many have been enthused or excited, but others have been concerned or even frightened. What has become clear to almost everyone is something we noted four years ago—we are the first generation in the history of humanity to create machines that can make decisions that previously could only be made by people. This resource provides best practices for implementing responsible AI systems.

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As artificial intelligence (AI) becomes more and more present in our lives, there is a greater impetus for leaders to engage in inclusive and equitable AI practices. This playbook shares good practices for leaders to advance equity and inclusion in AI and machine learning (ML) language.

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This playbook shares processes and best practices required to complete high-quality and ethical data science and AI projects for social impact organizations.

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Nonprofits increasingly recognize the value of data. It can help guide decisions, provide greater insight into your impact, and help make your organization more effective and efficient. Along an organization’s data maturity there comes a point when organizations start considering investing in artificial intelligence. For many though, it can feel like a minefield of acronyms, vaporware, and tools. This guide will help you understand what artificial intelligence (AI) is, how it is being applied in nonprofit settings, and where you might consider getting started.

In this Guide

• Defining artificial intelligence (AI)
• Examples of nonprofits using (AI)
• How to get started with machine learning (ML)

Conclusion

Artificial intelligence and machine learning are powerful tools for making the most of your data. They can help automate processes, improve your impact, and drive greater insight into operational effectiveness and overall impact. The tools to leverage the power of machine learning have never been more accessible, and more organizations than ever are finding ways to use machine learning to further their mission.

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This resource was created to help those in the nonprofit sector interested in exploring artificial intelligence (AI) and incorporating it into their work, providing questions to ask at each stage of AI implementation. This deck draws on insights from past and current examples of the nonprofit sector leveraging AI and has been informed by a diverse group of stakeholders, including NetHope members, UN agencies, technology providers, donors, and researchers.

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Artificial intelligence (AI) is the simulation of human thought processes in a computerized model. AI involves self-learning systems that use data mining, pattern recognition, and natural language processing to mimic the way the human brain works. To that end, designing for AI requires new considerations. The focus must be unwavering in providing experiences that put the user above all else. This resource provides guidance and best practices for data science teams to ethically design and develop AI projects.

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Machine learning (ML) is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence (AI) based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention.

Because of new computing technologies, ML today is not like the machine learning of the past. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks; researchers interested in AI wanted to see if computers could learn from data. The iterative aspect of ML is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. It’s a science that’s not new – but one that has gained fresh momentum.

This resource is designed primarily for data scientists or analysts interested in identifying and applying ML algorithms to address identified problems.

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