How and why non-profits can and should deploy artificial intelligence today

What is Artificial Intelligence?

The capability of a machine to

imitate intelligent human

behaviour

What is Machine Learning?

– Machine learning is an application of artificial intelligence

that enables systems to automatically learn and improve

from experience without being explicitly programmed

– Machine learning focuses on the development of computer

programs that can access data and use it to learn for

themselves

So…

– We talk a lot about AI

– We really need to deploy machine learning

– Which is semantics, the difference doesn’t really matter to us

now

– We need to simplify the narrative so we can access the

benefits

– We need to talk about Data (Quality & Governance)

What can AI do for me?

– Discover insights by comparing some

form of input with data they already

have

– Do statistical evaluations to predict

outcomes

– Recommend next steps based on the

results of their evaluations

– And some AI applications will

automatically implement actions

themselves

Why does this help?

– Do you have a mission-related question

to ask and have answered by your data?

– Do you have access to usable data?

– AI/ML gives you the ability to use your

data at scale, at pace

Should I?

– Don’t just think you have to

– Don’t do it because others are

– Don’t do it then work out why

– Learn from others

– Agree objectives and measures

– Trial, learn, commit

Routine

administration

– Chatbots!

– customer service and routine request

– manage first-line support queries and direct

queries to (the right) humans as needed

– automate repetitive tasks, reducing the risk of

input errors, accelerating data collection and

delivering a consistent experience

Saving you time

– you and your team can focus on what you do best

– AI handles what it does best, the time-consuming

work of data entry and analysis.

– You spend less time dealing with the tedious

details of scouring data and manually

implementing technology-embedded processes and

more time doing meaningful work

Supporting the cause

– Access to more sophisticated metrics

– Often displayed better

– To better understand audience and impact on

attitudes and behaviours

– Metrics aren’t everything, but they can help to give

an overview of the bigger picture at times

– New ways of displaying information may help

constituents, members or funders to better connect

Finance & Governance

– Fraud and corruption are major challenges

– Impossible to monitor every financial transaction and business contract.

– AI tools can help managers automatically detect actions that warrant additional investigation.

– AI and ML create early warning systems, spot abnormalities, and thereby minimize financial misconduct.

– These tools offer ways to combat fraud and detect unusual transactions.

Animal Welfare

– PAWS, an organisation dedicated to combating

poaching

– Using modelling and machine learning to give

park rangers the information they need to predict

poachers’ actions and stop them.

Fighting Abusive

Behaviour

– Online trolls disrupt organisations and target

particular individuals.

– 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.

Saving vulnerable lives

– Crisis Text Line still implements a human-to-human

volunteer model, but has the largest open source

database of youth crisis behaviour in the US

– It uses AI to dramatically shorten response time for

high-risk texters from 120 seconds to 39.

– Crisis Text Line leveraged ML 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

Predictive Analytics

– “What is most likely to happen based on my

current data, and what can I do to change that

outcome?”

– Use historical data, machine learning, and AI to

predict what will happen in the future.

– Minority Report was released in 2002…

Trend Analysis

– Passive

– Not answering questions, but finding trends

– As data is added, built up, trends are identified

– Not constrained by parameters we set or

subconscious predeterminations

Personalisation

– Looks at past interactions, e.g. what they viewed on your website, links clicked in emails and social updates etc.

– Analyse data, spot patterns, and learn what works best for different segments of your audience

– Craft your appeals and communications based on what prospects or donors care about most.

– Email opens will increase and unsubscribes will decrease when you know

– What’s the best time to ask a one-time donor to give again?

– What’s the best communication channel to use with a particular donor?

– Which stories resonate most with a particular donor?

– Passive evidence-based, not assumed / expected, and continually tuned

Engagement Scoring

– Currently we set metrics, assign scores, measure

– AI can use more evidence to do this better

– To better assign value of activity in line with

outcomes it can see

Fundraising

– The key to successful non-profit fundraising and AI is teamwork.

– Now human fundraisers team up with AI assistants, with each half doing what they do best.

– The AI assistant develops and applies algorithms to ingest, clean, enrich and search vast amounts of data to recognize patterns and give non-intuitive recommendations.

– The human fundraiser applies judgment and context to select from those recommendations to reach out to the right donor with the right message.

Knowledge Management

– The ability to consume vast quantities of data in an

instant

– To profile match the requester with deep mining of the

information

– Long been the key to membership recruitment and

retention

– The Holy Grail for esteemed membership bodies who are

now having to compete with Google to be the font of all

knowledge

– Data Governance and Quality becomes the imperative

Preventative Diagnostics

– AI (IBM’s Watson) saved a woman’s life through early

diagnosis of a rare form of leukemia by comparing her

genetics to 20 million oncology studies. In 10 minutes!

– King’s College is revolutionising the radiotherapy industry

and treatment process. AI will be used to bolster an existing

workforce of trained medical professionals.

– An AI is now capable of detecting the early signs of

Alzheimer's Disease; picking up early signs an average of 6

years before human physicians are able to issue a diagnosis.

– An AI learnt how to model and predict heart disease

mortality rate in patients.

“Deep Patient”

– Analysed 700,000 patient records, with no framework

understanding to hang it all on

– Was able to make more accurate diagnosis than human

physicians.

– Algorithms work because they capture, better than any

human can, a universe in which everything affects

everything else, all at once.

Digital Health Assistant

– Reason Digital, Parkinson’s UK, the Stroke Association,

Muscular Dystrophy UK and the MS Society

– Transforming the way medical advice and information is

delivered to almost half-a-million people in the UK.

– Use machine learning to

– develop an understanding of the person being supported

– adapt to their needs over time based on interactions.

– DHA will provide emailed content and support specific to

the individual’s needs, making it more effective

What does this tell us

– There are lots of different applications of

the same core capabilities and principles

which can help you

– Achieve your mission

– Serve your members or supporters

– Support your beneficiaries

– Do some good

Yes you can!

– You need mission-related questions to

ask and have answered by your data

– You need access to usable data

– You need expertise to build the

algorithm for predictive models

– You need a strategy and plan to manage

ethical concerns around privacy & data

bias

Next steps…

– Start tracking and collecting data

– Ensuring that the data going into your

systems is clean and deduped

– Develop a strategy for the what and why

– Agree your priorities

– Initiate a change programme

Rob Dobell

Hart Square#techsmartnfp

rob.dobell@hartsquare.co.uk