Allison Fine, my co-author for the Networked Nonprofit, and I are actively researching the use of AI for Good, in particular to scale giving and to spread generosity. We are most excited about the human side of AI and how it will augment the work of fundraisers and create a better experience for donors.
What about your nonprofit? Are you trying to get up to speed on AI and other emerging technologies and how they might impact your work? Want to better understand the use cases for AI and nonprofits in fundraising, programs, and other areas? These E-books, research, and articles will get you up to speed.
AI4GOOD NONPROFIT READS
State of AI in Advancement Survey: According to the Nonprofit Times article on the survey, AI Uses Among Nonprofits is still in the very early stages. About two in five nonprofits (42 percent) report researching AI but less than a third (28 percent) said that AI is either deployed, in the implementation phase, or experimental. The survey will be implemented regularly to benchmark adoption of AI.
AI for Good: Nonprofit Trends and Use Cases is an e-book published by Salesforce.Org that provides a good primer about AI, a few basic examples of how nonprofits are using Salesforce.Org product, Einstein, and useful tips on ethical AI.
What Your Nonprofit Needs To Know About Machine Learning from Global Giving’s Data Scientist Nick Hamlin. Really good set of questions for your nonprofit to ask about Machine Learning to see the value or benefit. The questions include:
- Do you have a mission-related question to ask and have answered by your data?
- Do you have access to usable data?
- Do you have access to people with expertise to build the algorithm for predictive models?
- Do you have a strategy and plan to manage the ethical concerns around donor privacy or data bias?
- Is it worth it?
Demystifying Machine Learning for Global Development, an article published in the Stanford Social Innovation Review, suggests that when it comes to global development, the key is to ask the right questions, and then see if and how it can help. The list of questions with examples in this article include:
- How can written information better support our decision-making?
- How can provide the right person with the right information?
- How can we predict events, behavior, or market dynamics?
- How can we better understand cause and effect?
The challenges include: good data; expertise; context; and better understanding the tools. The author, Sema K. Segair, is the founder of Surgo Foundation where you’ll find some excellent resources and articles on the topic of data, AI, and Global Development.
Solving Humanities Problems With AI: A Quick Guide, an article from the UK Charity News, interviews Microsoft’s AI for Good program staff. The program is a new global initiative to apply Artificial Intelligence (AI), machine learning and data science to solve humanitarian issues, advance global sustainability, and amplify human capacity with these new technologies. The way it works is that Microsoft provides the technology and expertise to those nonprofit with a vision and ideas of applying the technology to solve problems.
How AI Can Improve Your Nonprofit Fundraising Channels, an article by Boodle.AI Strategy Officer Frances Hoang emphasizes that the key to successful nonprofit fundraising and AI is teamwork. Human fundraisers team up with AI assistants, with each half doing what they do best. First, the AI assistant develops and applies algorithms to ingest, clean, enrich and then searches through large amounts of data in order to recognize patterns and give non-intuitive recommendations. Then, the human fundraiser applies judgment and context to select from those recommendations to reach out to the right donor and with the right message. Hoang suggests that this Human-AI team is the future of nonprofit fundraising.
THOUGHT PIECES AND FUTURE SCENARIOS
AI is here and now. What is under development and where it is heading? Here’s a few pieces that consider the impact of AI’s future development based on current research and examples.
Artificial Emotional Intelligence: Decoding human emotions is one of the recent examples of how researchers are exploring AI according to this C-NET article. EmoNet, neural network model developed by researchers at the University of Colorado and Duke University, was accurately able to classify images into 11 different emotion categories. The study might provide value to researchers who were previously dependent on participants self-reporting their emotions. Now instead of only relying on subjective responses, scientists can focus on patterns within the visual cortex using AI to better understand a subject’s feelings. This has implications in the mental health field.
But, think about it – if you had a database of images and knew what the emotional responses was from people. Could you use that dataset (with other data) and create a predictive model for what imagery might generate the right emotional response to trigger a donation or engage a donor for a Facebook Ad, email appeal or other content.
Can Machine Learning Widen Gap Between Knowledge and Understanding: David Weinberger describes a “deep learning” program called Deep Patient that has analyzed 700,000 patient records, with no framework understanding to hang it all on. Yet, it was still able to make more accurate diagnosis than human physicians. He notes that “Deep learning’s algorithms work because they capture, better than any human can, the complexity, fluidity, and even the beauty of a universe in which everything affects everything else, all at once.But this benefit comes at a price: We need to give up our insistence on always understanding our world and how things happen in it.” This article is an excerpt of Weinberger’s recently published book, Everyday Chaos.
We are well into the age of automation – bots, artificial intelligence and virtual assistants, but still in early phase for the nonprofit sector. There are many experiments taking place within the nonprofit to integrate AI for programs, fundraising, and internal operations, many in partnership with the big tech companies.
And while many of us in the nonprofit sector do not need to learn to code algorithms, we do need to understand what they can do and how it may be useful or harmful to nonprofit missions and stakeholders. We also need to look at the broader impact on civil society.
It is the next phase of digital transformation – from networks to artificial intelligence. How is your nonprofit preparing and understanding this transformation on your programs, audiences, and work? Does your nonprofit have experiments taking place right now? Please do share in the comments.