6 real-life examples of Machine Learning deployments for non-profits

Artificial Intelligence and Machine Learning are two terms we hear a lot, are often treated as interchangable, and sometimes mis-represented, but for all that, they are technology innovations which have the potential to radically improve the quality of life of individuals the world over.

At TechsmartNFP 2019 I gave a talk which shared a number of real-life examples to show how non-profits have been able to put AI and/or ML to good use already, from some basic everyday communications applications, to some implementations which appear to belong in the latest sci-fi blockbuster.

Featured here I wanted to give 6 real-world applications which non-profits and their beneficiaries are benefiting from right now, which are accessible and adaptable to many a charity or health-related network today.

The final section of the article then describes how AI-based Chatbots can be deployed to help deal with the overhead of routine administrative tasks, so hopefully there’s something here for everyone!

Troll Patrol

With digital communications tools readily available to everyone, online trolls can disrupt organisations and target particular individuals. To tackle this issue, Amnesty International pioneered a machine learning and crowdsourcing tool that can spot online abuse automatically and enable organisations to remove it.

The Troll Patrol can identify racist, sexist, or homophobic tweets, among other objectionable content and eliminate the abuse. In an era of polarized rhetoric and hate speech across all channels, but especially on Social Media, this is highly relevant for non-profits.

Crisis Text Line

Crisi Text Line still implements a human-to-human volunteer model, but the tech non-profit has the largest open source database of youth crisis behaviour in the US, and has been able to use AI to dramatically shorten response time for high-risk texters from 120 seconds to 39.

Crisis Text Line leveraged machine learning to identify the term “ibuprofen” as 16 times more likely to predict the need for emergency aid than the word “suicide.” Now using AI, messages containing the word “ibuprofen” are prioritized in the queue.

Protecting Endangered Animals

PAWS, is an application developed by a team of researchers dedicated to combating poaching. The application is using modeling and machine learning to give park rangers the information they need to predict poachers’ actions and stop them.

Digital Health Assistant

Reason Digital is teaming up with Parkinson’s UK, the Stroke Association, Muscular Dystrophy UK and the MS Society to develop the Digital Health Assistant project, which is set to transform the way medical advice and information is delivered to almost half-a-million people in the UK.

The Digital Health Assistant (DHA) will use machine learning to develop an understanding of the person being supported and continues to adapt to their needs over time based on interactions. This allows DHA to provide emailed content and support specific to the individual’s needs, making it more effective than current alternatives.


Text4baby, a free nonprofit service from Wellpass in cooperation with CTIA Wireless Foundation, uses a chatbot to provide critical health and safety information for pregnant women and mothers with infants in the US.

This chatbot covers an impressive array of critical topics including nutrition, immunization, breastfeeding, and car seat safety, and is available in English or Spanish. Those who use the app receive free text messages three times per week, timed to their baby’s due date or birth date, from pregnancy up until their baby’s first birthday.

Finance, Governance and Fraud Detection

Fraud and corruption are major challenges for any kind of organization as it is hard to monitor every financial transaction and business contract.The non-profit sector is facign ever-greater scrutiny of its behaviours and governance, with the damage caused by any negative publicity potentially disastrous for individuals and causes alike.

AI tools can help managers automatically detect actions that warrant additional investigation. Businesses long have used AI and ML to create early warning systems, spot abnormalities, and thereby minimize financial misconduct. These tools offer ways to combat fraud and detect unusual transactions.

Routine Administrative Tasks

AI-based Chatbots, which we’ve all encountered, automate conversations for commonly asked questions through text messaging or telephone. A chatbot is a type of software that produces intelligent, automated responses to common questions in order to hold a “conversation” with a user. It stands to follow then, that AI algorithms can enable efficient and effective communications with both internal and external audiences.

Chatbots can help with customer service and routine requests, such as how to contribute money, address a budget question, or learn about upcoming programs. They can manage first-line support queries and subsequently direct those queries to human personnel as needed. Chatbots can even schedule appointments.

In addition, AI can automate repetitive tasks, reducing the risk of human inputting errors, accelerating accurate data collection and ensuring an organization’s donor outreach is seamless and timely.

Schedule and reschedule meetings, send out briefings, set reminders – AI is primed to handle these types of routine obligations and applications already exist to manage these tasks. A message to schedule, postpone, or cancel a meeting is sent to an office bot, via SMS or other software-enabled tool, and the bot first scans a person’s calendar before scheduling the meeting. Then, it automatically sends alerts to involved parties.

AI completes the task, saving time, labour and flaws of human involvement. Afterward, it can automatically send meeting minutes to all involved parties, arrange introductions among individuals, and even book travel. That’s pretty handy and supremely efficient


This gives a few simple examples which show that non-profits have been able to harness AI/ML to deliver better services, or deliver them more widely, or more consistently, and are intended to encourage others to follow suit. This entails identifying appropriate use cases for your non-profit, exploring possibilities, experimenting, learning, tuning and trying again. It takes elements of bravery, degrees of effort and a willingness to fail then start over, but the opportunities do make that a worthwhile investment.


The drive for non-profits to really engage with AI

Despite the potentially off-putting hype and noise around Artificial Intelligence and “the rise of the machines” the reality is that AI and machine learning are technologies which have arrived and are on the verge of being mainstream.

Projects to evaluate, implement and deploy these technologies are now both appropriate and affordable, and whilst they must of course be treated with caution, they now represent arguably the biggest opportunity for non-profits who are striving to stay relevant and to radically enhance the services and benefits they offer to their supporters, members and beneficiaries alike.

What does this mean in practice?

The deployment of AI and ML technology can mean many things but the real benefit they bring to non-profits is in the ability they offer to mine and manipulate data at scale. Data is the lifeblood of non-profits; whether that’s to be able to understand more about donors and supporters and thereby to create deeper, more valuable relationships, or whether it’s used to analyse vast quantities of data in ever-decreasing timeframes, to identify and provide back critical information to beneficiaries or service users.

In the latest example of this, delivering a ground-breaking innovation, Muscular Dystrophy UK, Reason Digital, Parkinsons’ UK, the Stroke Association, and the MS Society have joined in an unprecedented partnership to harness the power of AI for good, creating the UK’s first AI health assistant. The Digital Health Assistant (DHA) is set to transform the way medical advice and information is delivered to millions of people in the UK.

The DHA will use machine learning to develop an understanding of the person being supported and continues to adapt to their needs over time based on interactions. This allows DHA to provide emailed content and support specific to an individual’s needs, making it vastly more effective than current alternatives.

This real-world implementation of AI for good, by a coalition of charities, spells out the opportunity for every non-profit to innovate and to harness the latest technologies in support of their cause. The technology is now science-fact and our challenge is to be brave enough to embrace it, to put it to use, and to derive a series of benefits for the whole of society.


This article was first published by Synergy in print format

AI: Bringing people together or the end of the world as we know it?

It is predicted that in the next 50 years we will have built a brain that is smarter than us in every way. This will probably be the last thing we ever create and nobody knows what will happen. It keeps me awake at night and is probably the largest existential threat to humanity, leading AI expert Daniel Hulme told delegates at TechSmart 2019.

Daniel, who is CEO of Satalia and also runs a masters programme at UCL (University College London), views the world of artificial intelligence from both an academic and commercial viewpoint.

“There are two definitions that people often have of AI – the first one is popular and weak: getting computers to do things as good as or better than humans do. The second which is much better, is that AI is goal directed adaptive behaviour that works towards an objective. Good AI learns from its decisions both good and bad, and adapts its behaviour accordingly.”

Often, Daniel told delegates, companies don’t have technology or machine learning problems, they have decision-making problems and many are beginning to realise they have people working for them who have the wrong skills or the culture of the organisation does not motivate and inspire.

“We need organisations with a strong purpose which are empowering people for the future. It is not just about the technology – attracting the right talent is the most important thing. The challenge is how to attract this talent. You need the right culture to enable talent to thrive; traditional hierarchies are not conducive to innovation and the faster you can adapt the better.”

Data is only useful of you have it all in one place, analyse it, and by finding patterns you gain insight which allows you to find out not only what is happening but why, and then gives you the ability to make predictions and take action. “When I build AI solutions they usually have at least these three components – data, insight and action,” says Daniel.

Computers are usually very effective at finding patterns but not so good at adaptive decision-making and solving optimisation problems. This is because they understand what is happening but not why. “Computers can study masses of data and solve some problems in seconds, such as planning a delivery route. The point is that understanding is not what the computer does – people are needed to understand what the data means.

“And when we build the brain that is smarter than us it may remove us from the equation if we are not working together.”

If you give a machine the task of eradicating cancer it may well decide that to do that you need to kill all humans. We have to give machines the right objectives and constraints