“It is more difficult to give money away intelligently than to earn it in the first place.” – Andrew Carnegie
If you want to invest your money in stocks, there is no shortage of companies, experts and advisors with opinions of how to invest your money. If you want to donate to a charity, there is a shortage of experts and advisors advising to whom or what you should donate. Of course, most organizations have websites with pictures and statements enticing you to donate or become involved. Some even employ fundraisers who are specifically charged with finding donors to support the organization.
But what if you can’t decide where to donate your money? What if, at the end of the day, all the websites and noise about who produces the greatest impact just end up becoming a giant distract? What if all the nice pictures and smiling faces start to look the same, and eventually organizations themselves start to look the same? Where do you put that money? What if you’re not one of the smartest philanthropists out there: Bill Gates and you can’t afford 1,000+ people with PHD’s to assist you in identifying the most impactful use of your dollars?
So many diversions and so many questions to ponder for the potential donor. Enter machine learning. Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Machine learning is already being used in multiple domains and industries with alot of money at stake. Not so much in the philanthropic sector though. Even so, for the charitable donor, it offers enormous potential as a way to help break through the noise. In doing so, it can serve as a means to amplify the impact of your charitable dollar. Though a few years away yet, let’s observe three ways machine learning can advance giving and ease the donor burden.
1. Machine learning can tell you which agencies support your chosen cause. It’s easy to align cause with agency through machine generated insights from all the online activity out there.
2. Machine learning can give you an idea of who is supporting your chosen cause. While you may stumble across an interesting page online with what looks like a worthy cause, why not rely on the past actions of donors and perhaps even your friends for your next giving decision? With no shortage of social media posts from people showcasing their attendance at a fundraiser or posts bringing attention to recent donations via gofundme.com, machine learning can easily tap into social media platforms to guide you.
3. Last, you may hone in on quality by quickly ranking local agencies via online ratings. Guidestar and Charity Navigator actively promote their rating systems. While the basis for these ratings always sparks discussion, outliers (good and bad) on their scales will stand out in any machine learning generated findings.
Many of these solutions require development of new but quite simple algorithms and there are quite a few barriers, some of which are eroding as you read thanks to efforts like Guidestar’s Financial SCAN (Situation and Comparable Analysis). Geocoding and the new IRS Tax Exempt Organization Search, which kicks off whenever the IRS can fix its website, offer new unlimited potential for donors to target their dollars to effective programs and initiatives.
But more and more, donors probably do not want to have to wade through multiple platforms and IRS legalese just to figure out an answer to the simple question: who is worthy of my support? More and more, new donors want faster insights and more rapid feedback. Considering the possibilities to inform donors, the application of machine learning to philanthropy is inevitable.