Summary of data for good and AI for good initiatives and challenges

1. Technology / Artificial Intelligence for Good
Lecture Summary, TU Kaiserlautern, 2018
Frank Kienle
Data / Artificial Intelligence for Good
Lecture, TU Kaiserslautern, 2019

2. Data / Artificial Intelligence for Good
Setting the right goals
Frank Kienle

3. Ethics is knowing the difference between what you
have a right or the power to do and what is the right
thing to do (Potter Stewart)
16/03/2019 Frank Kienle p. 3

4. https://autonomousweapons.org/slaughterbots/
BAN LETHAL AUTONOMOUS WEAPONS
Slautherbot Video to demonstrate the danger!
16/03/2019 Frank Kienle p. 4

5. The original Landlord's Game (1903, Elizabeth Magie ) had the
object of showing that rents enriched property owners and
impoverished tenants.
Key message and objective:
Monopoly was invented to demonstrate the evils of capitalism
Challenge of setting the right goals for AI
Same rules different values will lead to different results
16/03/2019 Frank Kienle p. 5
Game rules today (still the same):
The winner takes it all, whoever managed to bankrupt
the rest emerged as the sole winner
Key message today:
Chase wealth and crush your opponents if you want to
come out on top

6. 17 agreed goals for a better world
https://www.globalgoals.org
16/03/2019 Frank Kienle p. 6

7. 17/03/2019 Frank Kienle p. 7
23 ASILOMAR AI PRINCIPLES (2017)
(HTTPS://FUTUREOFLIFE.ORG/AI-PRINCIPLES/)

8. Research Goal towards
beneficial Intelligence
23 ASILOMAR AI PRINCIPLES (2017)
(AROUND 4000 RESEARCHERS /SUPPORTERS SIGNED ALREADY THESE PRINCIPLES)
16/03/2019 Frank Kienle p. 8
Accompanied
research funding
Active Science-
Policy Link
Transparent
Research Culture
Race Avoidance
so prevent safety
short-cuts
Safe and verifiable
through life cycle
Failure
transparency
Judicial
transparency
Responsibility of
designers and builders
Behavior aligned
with given values
Aligned with
Human Values
Right of
Personal Privacy
Ensure people’s
liberty
Shared Benefit
for many
Shared prosperity
for society
Human control on how
and whether to delegate
Non-subversion
of society
Avoidance of
AI arms race
Research
Issues
EthicalIssuesLongTerm
Issues
Caution on AI
capability
assumptions
Importance and
managed care
of changes in life
Strong mitigation
efforts for posed
existential risks
Strict safety for
Self-Improving
system
Development of
superintelligence
in the service of
common good

9. ...use data to not only make better decisions about what kind of movie we want to see,
but what kind of world we want to see..
Data (Science) for social good movement
Example DataKind (http://www.datakind.org)
16/03/2019 Frank Kienle p. 9

10. (Excerpt) AI safety research teams
AI safety will play a crucial role in the future and is so far an
unsolved problem
16/03/2019 Frank Kienle p. 10

11. Data / Artificial Intelligence for Good
Trust is a building block of society
Frank Kienle

12. Tech companies are getting more and more under pressure
https://www.nytimes.com/interactive/2017/10/13/opinion/sunday/Silicon-Valley-Is-Not-Your-Friend.html
17/03/2019 Frank Kienle p. 12

13. Fake news…. describe content published by established news providers that they
dislike or disagree with, but is more widely applied to various types of false
information, including: *
• Fabricated content: completely false content;
• Manipulated content: distortion of genuine information or imagery, for example a
headline that is made more sensationalist, often popularised by ‘clickbait’;
• Imposter content: impersonation of genuine sources, for example by using the branding of
an established news agency;
• Misleading content: misleading use of information, for example by presenting comment as
fact;
• False context of connection: factually accurate content that is shared with false
contextual information, for example when a headline of an article does not reflect the
content;
• Satire and parody: presenting humorous but false stores as if they are true. Although not
usually categorised as fake news, this may unintentionally fool readers.
* Source: Disinformation and ‘fake news’: Interim Report - Digital, Culture, Media and Sport
Committee - House of Commons
Fake news: misinformation and misinformation
16/03/2019 Frank Kienle p. 13

14. Building a quality media ecosystem
A fact-checking community,
leveraged by artificial intelligence
Fake news challenge tackled by AI companies
16/03/2019 Frank Kienle p. 14
Detection of:
• Hate speech and abusive content
• Propaganda and extremely politically
biased content
• Spoof websites and content spread
by known fake news networks
• Extreme clickbait content
Avantgarde Analytics: AI AGAINST FAKE NEWS
We use AI to combat fake news and false amplifiers. Using state-of-
the-art intelligent systems, our goal is to prevent the spread of
inaccurate or manipulated information online.
We fight against computational propaganda that attempts to distort
political sentiment. Our coordinated campaigns help voters see the big
picture and discover diverse political content. We support the core
principles of democracy and reinforce civic engagement with machine
learning technology.

15. #MacronLeaks: Autonomous bots swarmed Facebook and Twitter with leaked
information that was mixed with falsified reports, to build a narrative that Macron
was a fraud and hypocrite
Fake news spread and speed are managed by bots
16/03/2019 Frank Kienle p. 15
Source: https://goo.gl/hGvGWf

16. Verification handbook for journalists during emergencies
16/03/2019 Frank Kienle p. 16
A definitive guide to verifying digital
content for emergency coverage
Authored by leading journalists from the
BBC, Storyful, ABC, Digital First Media and
other verification experts, the Verification
Handbook is a groundbreaking new
resource for journalists and aid providers.
It provides the tools, techniques and step-
by-step guidelines for how to deal with
user-generated content (UGC) during
emergencies.
http://verificationhandbook.com
Book includes a large list of tools

17. Disinformation and ‘fake news’: Interim Report - Digital, Culture, Media and Sport
Committee - House of Commons
(https://publications.parliament.uk/pa/cm201719/cmselect/cmcumeds/363/36302.htm)
Political reactions, e.g. UK House of Commons
16/03/2019 Frank Kienle p. 17

18. Our goal is to set up one-on-one discussions between people with completely
different viewpoints – thus establishing a new form of political debate. Together
with our partners, we hope to initiate debates in many countries around the world.
Technical support to foster diverse political dialog, breaking
the media ,filter bubble’
16/03/2019 Frank Kienle p. 18
e.g. https://www.zeit.de/serie/deutschland-spricht
Answer yes /no
questions
Analyze,
classify
persons
Match
diverse dialog
partner
Enable Self-
organized
dialog

19. Style transfer techniques will be a serious problem in the
future
16/03/2019 Frank Kienle p. 19
Building tools to authenticate that information, and
rebuild trust in what you see on the internet.
https://youtu.be/cQ54GDm1eL0 https://youtu.be/XOxxPcy5Gr4

20. Data / Artificial Intelligence for Good
Jobs
Frank Kienle

21. We know a lot more - more than we can tell,
and we can’t automate what we don’t understand
If you can describe your job it will be automated
The new trend is the intelligent support / augmentation of ‘white-collar’ jobs
AI (artificial intelligence) vs IA (intelligence amplification)
Many (ethical) open questions exist:
• What should be and not be automated
• What should be connected or not be connected
We can know more than we can tell (Polanyi Paradox)
17/03/2019 Frank Kienle p. 21

22. How Kiva robots automate a warehouse environment (Amazon)
16/03/2019 Frank Kienle p. 22https://www.youtube.com/watch?v=6KRjuuEVEZs

23. Next generation robots: Boston Dynamics, Asimo, Da Vinci, SoFi
https://www.youtube.com/watch?v=8vIT2da6N_o&frags=pl%2Cwn
16/03/2019 Frank Kienle p. 23

24. The qualification profiles of jobs will change rapidly
16/03/2019 Frank Kienle p. 24

25. The future of jobs and new roles
(http://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf
16/03/2019 Frank Kienle p. 25

26. Advise for Students:
Focus less on efficiency and
more on creating new values
for humans
Every project should think
about its link to ethics and its
impact on our society
In Summary, the left part of the brain (logical part) will be
replaced by AI
16/03/2019 Frank Kienle p. 26

27. Do not pay: world's first robot lawyer.
Fight corporations, beat bureaucracy and sue anyone at the press of a button
16/03/2019 Frank Kienle p. 27

28. Data / Artificial Intelligence for Good
DATA Challenges (People, Data, Profit)
Frank Kienle

29. Clickbait problem – the internet /social media disaster
16/03/2019 Frank Kienle p. 29
More clicks results in more (marketing) money
Sensational headlines / news beats facts
(beliefs eat facts for breakfast)
The internet has already destroyed our will to care if
something is real or fake in internet / news

30. Clickbait problem – the internet /social media disaster
16/03/2019 Frank Kienle p. 30
Example student work, clickbait generator: https://www.cs.uvic.ca/cbgeneration/#
Clickbait: content whose main purpose is to attract attention and encourage
visitors to click on a link to a particular web page.
Clickbait articles tend to run under 300 words, and don’t ordinarily include
original ideas or content, often just a collection of multiple sources
Headline and content can be 100% automized by natural language generation
techniques

31. Bots can easily generate seemingly unique sentences
16/03/2019 Frank Kienle p. 31

32. Automated content curation and generation will be the future
Example: Narrative Science (https://narrativescience.com)
16/03/2019 Frank Kienle p. 32

33. Brands and public identities are negatively impacted by
social media attacks
17/03/2019 Frank Kienle p. 33
Former Israeli manipulators of the social networks for business and political
campaigns have crossed the lines and are now helping to detect such campaigns.
....,negative campaigns and fake news are usually conducted by creating false
profiles and that this tool became common in both politics and business....‘
Source: https://en.globes.co.il/en/article-identifying-fraud-in-business-intelligence-1001246345

34. Data in web poll on net neutrality organized by FCC is very
polluted, where only 10% seems to unique comments
16/03/2019 Frank Kienle p. 34
http://webreprints.djreprints.com/4250450987596.html

35. AI will turn raw news content into automated insights
Example data source: Eventregistry (eventregistry.org)
17/03/2019 Frank Kienle p. 35

36. Data protection laws are important and will evolve
Status of world wide data protection laws (https://www.dlapiperdataprotection.com)
16/03/2019 Frank Kienle p. 36

37. 17/03/2019 Frank Kienle p. 37
Data Science & AI for good overview
https://carlgogo.github.io/AI4G_mindmap/

38. Excerpt of activities and sources and its key message
17/03/2019 Frank Kienle p. 38
Title Link Key Message
Slaughterbots https://www.youtube.com/w
atch?v=9CO6M2HsoIA
BAN LETHAL AUTONOMOUS WEAPONS
Future of Life https://futureoflife.org Technology is giving life the potential to flourish like never before... Or
to self destruct. Let's make a difference!
World Future Society https://www.worldfuture.org Future-minded citizens charting a new course for humanity.
OECD http://www.oecd.org/going-
digital/ai/
The OECD is putting significant efforts into work on mapping the
economic and social impacts of AI technologies and applications and
their policy implications.
AI for Good Foundation www.ai4good.org Climate Change, Corruption Transparency in Government, Education,
Employment and Skills, Food Energy Water, Gender Equality, Health,
Media Bias and Access
Global Goals https://www.globalgoals.org In 2015, world leaders agreed to 17 goals for a better world by 2030.
These goals have the power to end poverty, fight inequality and stop
climate change.
Future Agenda https://www.futureagenda.o
rg
Future Agenda is a not-for-profit programme that was first run in 2010
and repeated in 2015 to bring together views on the future decade
from many leading individuals and organisations.
Data Science for social
good (example)
https://dssg.uchicago.edu Training data scientists to tackle problems that really matter
Data Protection Laws https://www.dlapiperdatapro
tection.com
Example GDPR: General Data Protection Regulation 2016/679 is a
regulation in EU law on data protection and privacy for all individuals
within the European Union