This is a slide deck for a session I did for the European Foundation Centre in Dec 2018

1. 1
Machine Made Goods
Civil Society, Philanthropy & Artificial Intelligence
Rhodri Davies
Head of Policy & Programme Director, Giving Thought

2. Current Disruptive Technologies
2
Artificial Intelligence
Blockchain
Cryptocurrency
Big Data
3D Printing
Virtual & Augmented Reality
(VAR)
Internet of Things
(IoT)
Autonomous Vehicles &
Drones
CRISPR/ Biotech
Wearables
Robotics
Human
Augmentation
Quantum Computing

3. Why should funders and CSOs care
about disruptive tech?
3
New ways of achieving
mission
1)
Change the way
organisations operate
2)
Create new problems to
address
3)

4. 4
1) Tech for Good

5. AI for Good

6. Blockchain for good
6

7. VAR for Good

8. 8
Convergence: “Curing” Visual
Impairment?

9. 9
2) Impact of AI on
operating environment
for civil society

10. Artificial Intelligence (AI)
10
Number of key factors in recent AI growth:
1)More powerful
algorithms
(Deep Learning)
2) Data explosion
3) Greater
processing
power
4) Investment
NB: Narrow/Domain
Specific AI, not Artificial
General Intelligence
(AGI)
Yes No

11. Data & Transparency
“The world’s most valuable resource is no longer
oil, but data” The Economist (2017)

12. Data, Robotic Process Automation &
Grantmaking

13. AI & RegTech
Enhanced, automated due
diligence
Predictive/preventative
regulation
Continuous monitoring of legal
& regulatory environment
Risk modelling &
Predictive analytics
CAPABILITIES
APPLICATIONS
Automation of
processes
ML for risk profiling
and predictive models
Natural Language
Processing (NLP)
Using unstructured
data

14. Chatbots & Conversational AI
By 2020, the average
person will have more
conversations with
bots than with their
spouse. 30% of web
browsing will be done
by voice.
Chatbots will power
85% of all customer
service interactions
by the year 2020
Source: Gartner
Awareness
Info & Services
Donations
NONPROFIT APPLICATIONS

15. AI Philanthropy
Charity
recommender
algorithms
Social Impact
Robo-advisors
Collective
Intelligence
philanthropy
Philgorithms

16. Advice and Recommendations
We are increasingly accustomed
to algorithmic recommendations

17. Philanthropy Recommender
Algorithms
Charity recommendations
based on past preferences
or peer group behaviour
“If you liked Cancer
Research UK, you’ll
love RNLI!”

18. Making Philanthropy Advice Mass-
Market
18
“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

19. A bit of context
19

20. How likely is this to take off?
20

21. No really, how likely?
21

22. Philgorithms
22
Algorithm which identifies
most pressing needs at any
given time + most effective
interventions for addressing
them & effects automated,
rational matching of
philanthropic supply and
demand

23. Are Philgorithms Feasible?
23
You can’t remove the element of heart from charitable
giving, so this will never happen!
A) We will become
accustomed to
algorithmic advice in all
areas of life, so why
not charity?
Objection:
But…
B) There has always
been a desire among
some to make giving
more rational
C) There will be
contexts in which
giving is only
feasible without
human oversight

24. Making Philanthropy Rational
24
Then… …And now

25. The Machine-to-Machine (M2M)
economy
25
The Internet of Things market is going to be huge, with vast numbers of
M2M transactions
Could we harness some of this for philanthropy?

26. M2M Philanthropy?
26

27. M2M Philanthropy?
27

28. Collective Intelligence, Intelligence
Augmentation & “Centaur Philanthropy”?
28

29. Breakout 1
29
Where do you see the most likely short and long-
term impacts of AI on:
1) Your function?
2) Your organisation?
3) The external operating environment?

30. 30
3) Now for the
bad part…

31. Weaponised AI

32. Algorithmic Bias
When machine learning algorithms are taught using data sets that contain statistical biases
for e.g. race, gender, they exhibit and strengthen those biases over time

33. Filter Bubbles
• Technology such as social media
allows us to build ‘filter bubbles’
around our experience
• Likely to get worse as increasing
reliance on AI-based interfaces
tailors our experience of the world
to fit existing preferences and
biases.

34. Deepfakes
Crisis:
Response?Tech:
Law
Media:

35. The Attention Economy
“The only factor becoming scarce in a world of abundance is human attention.”
-Kevin Kelly
 Need to compete in this “attention economy” has led
to new problems:
How do charities compete for our attention without adopting techniques that
cause long-term harm?

36. Child Development
Current concerns:
Emerging concerns:
Future concerns?

37. Psychological Distance &
Desensitisation
Brundage, M. et al (2018) The Malicious Use of Artificial Intelligence:
Forecasting, Prevention and Mitigation

38. Inequality
Inequality is already a massive economic problem
Key question for development of tech: does it reduce or increase
inequality?

39. Key Cross-Cutting Themes
39
Disintermediation Networks & Platforms
Filtered experience
Radical Transparency
Digital Assets
Algorithms Data, data, data
Urbanisation
Inequality
New models for social good
Attention Economy
Automation of work
Ageing Population

40. Key Questions for Civil Society about
Disruptive Technologies
40
Will it offer new ways for
existing CSOs to run more
efficiently or effectively?
Could it give rise to new kinds
of donations?
Will it make it easier or
harder to identify potential
donors?
Could it give rise to entirely
new classes of donors?
Will it offer new ways of
engaging donors and
supporters?
Could the development of
this technology itself be
seen as a charitable cause?
Could it create new ways for
existing CSOs to solve social &
environmental problems?
Could it disrupt the
existing governance
structures of CSOs?
Could it create entirely new
problems that CSOs will have to
address?
Will it reduce or increase
inequality?
Could it create new challenges
for existing beneficiaries?
Will it lead to new
organisations emerging to
compete with existing CSOs?

41. What can Funders do about AI?
41
 Fund AI Research
 Partner with tech
firms
 Develop in-house
expertise
Harnessing the
Potential
Navigating the Operating
Environment
Addressing the
Negative Impact
 Pro Bono resources
 Country-level advocacy
 EU/UN influencing
 Work with tech sector
on FATML
 Support knowledge sharing
 Explore RegTech
 Mapping/evidence
of AI impact
 Upskilling civil society
 Take lead on open &
ethical data usage
 Assess automation
potential within your org
 Track public & private
sector uses of AI
 Support existing tech
for good initiatives

42. Breakout 2
42
What impact can you see AI having on
the people and communities you
serve?
How will you respond?

43. Where to find more
43

44. Rhodri Davies
Head of Policy & Programme Director, Giving Thought
Charities Aid Foundation
rdavies@cafonline.org