Based on report for the European Parliament Research Service: https://www.europarl.europa.eu/stoa/en/document/EPRS_STU(2019)624279

1. Regulating Disinformation
with Artificial Intelligence (AI)
St Andrews CompSci 4 April 2019
Prof. Chris Marsden (Sussex) &
Dr Trisha Meyer (IES, Free University of Brussels
VUB)

2. Sadly, not fake news
Chris Sonderby, FBK vice-president,
Christchurch livestream of 50 murders
1st user report 29mins after broadcast started,
12 minutes after massacre ended
Facebook created a digital fingerprint of
initial livestream,
bulk of the automatic removals
enabled 80% of the videos to be blocked
before they were publicly posted
Meaning 20% were not blocked

3. DRM technology to create the
fingerprints is fragile
Defeated by filming the screen and uploading the video.
E.g. 5 hours online: “New Zealand mass shooting”
Facebook recorded “more than 800 visually distinct videos”
 shared them with industry body,
 Global Internet Forum to Counter Terrorism,
 to let other companies take them down as well.
Neil Mohan, YouTube’s chief product officer,
cutting human reviewers out of the loop,
turned off feature that allowed users to search recent
uploads,
in order to block a route for new videos to be seen by
millions.

4. Neil Mohan made Jordan Peterson
& Yaxley-Lennon famous
 https://twitter.com/heyneil
“Google Paid This Man $100 Million: Here's His Story”
https://www.businessinsider.com/neal-mohan-
googles-100-million-man-2013-4?r=US&IR=T
“the visionary who predicted how brand advertising would fund
the Internet, turned this vision into a plan, and then executed it”

5. Fake news?

7. Defining disinformation
“False inaccurate or misleading information
designed, presented and promoted to
intentionally cause public harm or for profit”
In line with European Commission High Level Expert Group
We distinguish disinformation from misinformation,
which refers to unintentionally false or inaccurate information.

8. Facebook/Cambridge Analytica
via Northern Ireland

9. International Grand Committee on
Disinformation (“Fake News”)
 Leopoldo Moreau, Chair, Freedom of Expression Commission, Chamber of
Deputies, Argentina,
 Nele Lijnen, member, Committee on Infrastructure, Communications and
Public Enterprises, Parliament of Belgium,
 Alessandro Molon, Member of the Chamber of Deputies, Brazil,
 Bob Zimmer, Chair, and Nathaniel Erskine-Smith and Charlie Angus, Vice-
Chairs, Standing Committee on Access to Information, Privacy and Ethics,
House of Commons, Canada,
 Catherine Morin-Desailly, Chair, Standing Committee on Culture, Education
and Media, French Senate,
 Hildegarde Naughton, Chair, and Eamon Ryan, member, Joint Committee on
Communications, Climate Action and Environment, Parliament of Ireland,
 Dr Inese Lībiņa-Egnere, Deputy Speaker, Parliament of Latvia,
 Pritam Singh, Edwin Tong and Sun Xueling, members, Select Committee on
Deliberate Online Falsehoods, Parliament of Singapore
 Damian Collins, Chair, DCMS Select Committee, House of Commons

10. The Fog of War?
Carl von Clausewitz (1832) Vom Kriege
 “War is the realm of uncertainty; three quarters of the factors on which
action in war is based are wrapped in a fog of greater or lesser
uncertainty.
 A sensitive and discriminating judgment is called for; a skilled
intelligence to scent out the truth.”
 Russian Hybrid Warfare: A Study of Disinformation
 Flemming Splidsboel Hansen, 8 Sep 2017, Danish Institute for International
Studies (DII
 Marsden, C. [2014] Hyper-power and private monopoly: the unholy
marriage of (neo) corporatism and the imperial surveillance state,
Critical Studies in Media Communication Vol.31 Issue 2 pp.100-108
http://www.tandfonline.com/doi/full/10.1080/15295036.2014.913805
 Marsden, C. [2004] Hyperglobalized Individuals: the Internet,
globalization, freedom and terrorism 6 Foresight 3 at 128-140

11. Methodology: Literature &
Elite Interviews
Project consists of a literature review, expert interviews and mapping of policy
and technology initiatives on disinformation in the European Union.
 150 regulatory documents and research papers/reports
 10 expert interviews in August-October;
 took part in several expert seminars, including:
 Annenberg-Oxford Media Policy Summer Institute, Jesus College, Oxford, 7 Aug;
 Google-Oxford Internet Leadership Academy, Oxford Internet Institute, 5 Sept;
 Gikii’18 at the University of Vienna, Austria, 13-14 Sept;
 Microsoft Cloud Computing Research Consortium, St John’s Cambridge,17-18 Sept
 We thank all interview respondent and participants for the enlightening
disinformation discussions; all errors remain our own.
 Internet regulatory experts:
 socio-legal scholar with a background in law/economics of mass communications;
 media scholar with a background in Internet policy processes and copyright reform;
 reviewed by a computer scientist with a background in Internet regulation and
fundamental human rights.
 We suggest that this is the bare minimum of interdisciplinary expertise
required to study the regulation of disinformation on social media.

12. Artificial Intelligence? Machine
Learning Enhanced

13. Interdisciplinary study analyses implications
of AI disinformation initiatives
Policy options based on literature, 10 expert interviews & mapping
We warn against technocentric optimism as a solution to
disinformation,
 that proposes use of automated detection, (de)prioritisation, blocking
and removal by online intermediaries without human intervention.
 More independent, transparent and effective appeal and oversight
mechanisms are necessary in order to minimise inevitable
inaccuracies

14. Defining Automated Content
Recognition (ACR)
 Within Machine Learning techniques that are advancing towards AI,
 ACR technologies are textual and audio-visual analysis programmes
that are algorithmically trained to identify potential ‘bot’ accounts and
unusual potential disinformation material.
ACR refers to both
 the use of automated techniques in the recognition and
 the moderation of content and accounts to assist human judgement.
Moderating content at scale requires ACR to supplement human
editing

15. ACR to detect disinformation is
prone to false negatives/positives
 due to the difficulty of parsing multiple, complex, and possibly
conflicting meanings emerging from text.
 Inadequate for natural language processing & audiovisual
 including so-called ‘deep fakes’
 (fraudulent representation of individuals in video),
 ACR has more reported success in identifying ‘bot’ accounts.
 We use ‘AI’ to refer to ACR technologies.

16. What can AI do to stop disinformation?
Bot accounts identified:
 Facebook removed 1.3billion in 6 months
Facebook’s AI “ultimately removes 38% of hate speech-related posts”
 it doesn’t have enough training data to be effective except English-Portuguese
Trained algorithmic detection of fact verification may never be as effective
as human intervention:
 each has accuracy of 76%
 Future work might want to explore how hybrid decision models consisting
of fact verification and data-driven machine learning can be integrated
 Koebler, J., and Cox, J. (23 Aug 2018) ‘The Impossible Job: Inside Facebook’s
Struggle to Moderate Two Billion People’, Motherboard,
https://motherboard.vice.com/en_us/article/xwk9zd/how-facebook-content-
moderation-works

17. Zuckerberg on AI & disinformation
Some categories of harmful content are easier for AI to identify
and in others it takes more time to train our systems.
Visual problems, like identifying nudity, are often easier than
Nuanced linguistic challenges, like hate speech
 Zuckerberg, M. (15 Nov 2018) ‘A Blueprint for Content Governance and Enforcement’,
Facebook Notes, https://www.facebook.com/notes/mark-zuckerberg/a-blueprint-for-
content-governance-and-enforcement/10156443129621634/

18. Disinformation a rapidly moving target
 We analysed 250 articles, papers and reports
 strengths and weaknesses of those focussed on AI disinformation
solutions on freedom of expression, media pluralism & democracy
 We agree with other experts: evidence of harm is still inconclusive
 2016 US Presidential election/UK ‘Brexit’ referendum
 Investigated US Department of Justice and UK Parliamentary Committee

19. Legislators should not push this
difficult judgment exercise onto
online intermediaries
 Restrictions to freedom of expression must be
 provided by law, legitimate and
 proven necessary and as
 the least restrictive means to pursue the aim.
 The illegality of disinformation should be proven before
filtering or blocking is deemed suitable.
 Human rights laws paramount in maintaining freedom online

20. AI is not a silver bullet
 Automated technologies are limited in their accuracy,
 especially for expression where cultural or contextual cues necessary
 Imperative that legislators consider which measures
 may provide a bulwark against disinformation
 without introducing AI-generated censorship of European citizens

21. Different aspects of disinformation
merit different types of regulation
All proposed policy solutions stress the
importance of
literacy and
cybersecurity

22. Five recommendations
1. Media literacy and user choice
2. Strong human review and appeal processes
where AI is used
3. Independent appeal and audit of platforms
4. Standardizing notice and appeal procedures
Creating a multistakeholder body for appeals
5. Transparency in AI disinformation techniques

23. 1. Disinformation is best tackled
through media pluralism and
literacy initiatives
 these allow diversity of expression and choice.
Source transparency indicators are preferable over
(de)prioritisation of disinformation,
Users need to be given the opportunity to understand
 how their search results or social media feeds are built
 and make changes where desirable.

24. 2. Advise against regulatory action
to encourage increased use of AI
for content moderation purposes,
 without strong independent
 human review and appeal processes.

25. 3. Recommend independent appeal
and audit of platforms’ regulation
Introduced as soon as feasible.
Technical intermediaries moderation of content & accounts
1. detailed and transparent policies,
2. notice and appeal procedures, and
3. regular reports are crucial.
Valid for automated removals as well.

26. 4. Standardizing notice and appeal
procedures and reporting
creating a self- or co-regulatory multistakeholder body,
 UN Special Rapporteur’s suggested “social media council”
Multi-stakeholder body could have competence
to deal with industry-wide appeals
better understanding and minimisation of the effects of AI
on freedom of expression and media pluralism.

27. 5. Lack of independent evidence or
detailed research in this policy area
means the risk of harm remains far too high for
any degree of policy or regulatory certainty.
Greater transparency must be introduced
into AI and disinformation reduction techniques
used by online platforms and content providers.

28. Specific Policy Options:
The Co-Regulatory Triangle

29. Option and form
of regulation
Typology of regulation Implications/Notes
0 Status quo Corporate Social Responsibility,
single-company initiatives
Note that enforcement of the new General Data Protection Regulation and
the proposed revised ePrivacy Regulation, plus agreed text for new AVMS
Directive, would all continue and likely expand
1 Non-audited
self-regulation
Industry code of practice,
transparency reports, self-
reporting
Corporate agreement on principles for common technical solutions and
Santa Clara Principles
2 Audited self-
regulation
European Code of Practice of
Sept 2018; Global Network
Initiative published audit reports
Open interoperable publicly available standard e.g. commonly
engineered/designed standard for content removal to which platforms could
certify compliance
3 Formal self-
regulator
Powers to expel non-performing
members, Dispute Resolution
ruling/arbitration on cases
Commonly engineered standard for content filtering or algorithmic
moderation. Requirement for members of self-regulatory body to conform to
standard or prove equivalence. Particular focus on content ‘Put Back’
metrics and efficiency/effectiveness of appeal process
4 Co-regulation Industry code approved by
Parliament or regulator(s) with
statutory powers to supplant
Government-approved technical standard – for filtering or other forms of
moderation. Examples from broadcast and advertising regulation
5 Statutory
regulation
Formal regulation - tribunal with
judicial review
National Regulatory Agencies – though note many overlapping powers
between agencies on e.g. freedom of expression, electoral advertising and
privacy

30. Prior art....

31. Questions?