Slides from a talk I gave looking at the impact that AI could have on philanthropy advice and charitable giving. (NB: see accompanying notes too).

1. 1
Robotic Alms:
AI and the future of charitable giving
Rhodri Davies
Programme Director, Giving Thought

2. 4 Ways AI could transform charity
1) Creating new problems for charities to address
2) Developing new ways of addressing existing problems
3) Offering new ways of working for existing charitable
organisations
4) Creating new governance structures and operating models
for achieving social good
2

3. AI & Grantmaking
3
Some have been thinking about how AI could improve grantmaking process by
automating ID/selection of grantees, e.g. Nesta:

4. Philanthropy advice
4
From this… …To this

5. AI & Philanthropy advice
5
AI could transform philanthropy advice in a number of ways:
1) Mass marketisation: Automation through chatbots etc. could reduce costs & make mass-
market service, whilst also driving up quality
2) Identifying need: AI analysis of data could identify most pressing areas of social or
environmental need at given time
3) Picking interventions: AI analysis of social impact data could identify most effective
interventions and organisations
5) Understanding donor aims: AI analysis of donor behaviour, peer group etc. could help
to identify donor goals more effectively
4) Automated giving: AI could be used to automate matching of needs to interventions, and
dispersal of money; thus creating “AI philanthropy”

6. Making Philanthropy Advice Mass-
Market
6
“AI has the potential to become a
great equalizer. Access to services
that were traditionally reserved
for a privileged few can be
extended to the masses.”
PWC (2017) Bot.Me: A revolutionary
partnership: How AI is pushing man and
machine closer together

7. A bit of context
7

8. How likely is this to take off?
8

9. No really, how likely?
9

10. Advice on Philanthropy Methods
10
• Use AI-powered chatbots to bring together existing advice in
personalised way
• Might include:
• Vehicles (foundation, DAF, charity account etc.)
Social purpose org structures
(charity, CIC, socent, B Corp etc.)
Tax incentives (Gift Aid, Payroll
Giving, Share Gifts, SEIS etc.)
• Strategies (place-based, social
investment, venture philanthropy etc.).

11. Approaches to philanthropy advice
11
• Choice over whether to prioritise meeting
donor aims or maximising social outcomes
• May be aligned, but often there is tension
between the two

12. Focus on outcomes
12
• AI could analyse data to identify
most acute pressure points in terms
of social or environmental needs at
any given time.
Identifying causes Identifying Interventions
• AI could analyse data on social
impact to identify most effective
interventions/orgs
Onus on data:
Scale
Openness
Consistency
Social Impact data

13. How far will this go?
13
Hyper-rational “AI Philanthropy: AI used to ID
most pressing needs at given moment and match with most
effective interventions.
• So no need for human involvement
• Sceptical p.o.v: “can never remove element of heart from
philanthropy”
BUT: could this change in the future…?

14. Why should we care?
14
The IoT market is going to be huge, with vast numbers of M2M transactions
Q: Could we harness some of this for charity?

15. M2M Philanthropy
15

16. M2M Philanthropy
16

17. Identifying Donor Aims
17
• Subjective • Objective
• Self-reported info on what donor
wants to achieve
• BUT: We are notoriously bad at
self-analysis…
• Objective Social: Facebook
algorithm approach i.e.
advice/suggestions based on past
behaviour/peer group
• Danger of entrenched bias
• Filter bubbles
• Reinforcing popular causes and orgs.
• Objective Personal: Future in which
AI can know us better than ourselves
• Advice based on what objectively
maximises donor’s satisfaction.

18. Challenges
18
• Lack of tech skills in sector: how do we built partnerships, get govt support
etc.?
• Developing social impact data: who pays for it?
• Facebook-style algorithms: will they entrench social bias and create filter
bubbles?
• If algorithms are based on past behaviour, will popular causes/orgs win out
at expense of less popular ones?
• Black-box algorithms: how do you optimise if you don’t understand criteria?

19. What should charities do?
19
• Cross sector tech/AI forums (private sector, vol sec, govt)
• Join debate over AI and put charity perspective (e.g. CAF Lords
AI committee response.)
• Adopt Open Data approach (e.g. 360 Giving)
• Develop social impact data
• Work with IoT industry: both to harness data, and to develop
M2M giving

20. Where to find us
20
• CAF Giving Thought page: https://www.cafonline.org/about-
us/blog-home/giving-thought
• Archive Giving Thought blog: www.givingthought.org
• Giving Thought podcast: http://givingthought.libsyn.com
• Twitter @Rhodri_H_Davies

21. Rhodri Davies
Programme Director, Giving Thought
Charities Aid Foundation
rdavies@cafonline.org