Brazilian Virtual Seminar “Freedom of Expression and Online Content Moderation” 22 October 2020

1. Regulating Disinformation –
Regulatory Options
Professor Chris Marsden (Sussex)
Brazilian Virtual Seminar
“Freedom of Expression and Online Content Moderation”
22 October 2020

2. Specific Policy Options:
The Co-Regulatory Triangle

3. 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.

4. If policy wants
hard evidence of disinformation:
It does not and has never existed
Newspapers not proven to influence UK 1992
General Election
Disinformation not proven to influence US 2016
Presidential election
Motives are impure – evidence
threshold impossible

5. Online and offline media – which is
more influential and disinformed?
Should we focus on online media or core source of
misinformation for most people; mainstream media?
Cable TV news; newspapers; shock jock radio
 Noting elderly rely on existing media more than FBK
Pollution of online disinformation into MSM?
Online platforms are more important to tell us about
the weaknesses in the existing ecosystem
Digital canary in the media coalmine?
Use of MSM in online clips e.g. Bloomberg/deep fakes

6. Can misinformation affect the
normative context of public discourse
rather than disinformation directly
causing harm and
undermining democratic principles?
Data integrity vs discourse integrity?

7. General Hayman, former Director
of US National Security Agency

8. Governments
simply
identify
criticism as
‘fake news’

10. Can we vaccinate the public
against fake news?

11. Methodology 2018-2020:
Literature & Elite Interviews
Projects 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 2018;
 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. Interdisciplinary study analyses implications
of AI disinformation initiatives
Policy options based on literature, expert interviews & mapping
We warn against technocentric optimism as a solution to
disinformation,
proposes use of automated detection,
(de)prioritisation, blocking and
 removal by online intermediaries without human intervention.
Independent, transparent, effective appeal and
oversight mechanisms
 are necessary in order to minimise inevitable inaccuracies

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

14. Defining ACR
Automated Content Recognition
 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: 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 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. 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

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

20. 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,

21. Sources – Research Reports
Reports for
European Parliament 2019 (28 nations),
Commonwealth 2020 (56 nations),
UNESCO 2020 (everyone)
http://sro.sussex.ac.uk/view/creators/9904.html
Academic article:
[2020] Platform values and democratic elections:
How can the law regulate digital disinformation?
Chris Marsden, Trisha Meyer, Ian Brown,
Comp. Law & Security Rev. at
https://doi.org/10.1016/j.clsr.2019.105373

22. #KeepItOn Are national emergency
& superior court criteria effective?
Fragile democracies may have weak
executive or judicial institutions.
Who decides what is fraudulent?
May contribute to bias if enforced by
already distrusted institutions
Hungary, Poland
Current COVID19 live disinformation examples

23. Five EU 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

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

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

26. 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.

27. 4. Standardizing notice and appeal
procedures and reporting
creating 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 & minimisation of effects of AI
on freedom of expression and media pluralism.

28. 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.

29. Is there a regulatory approach to
design accountability into platforms?
See Shorenstein Report &
Santa Clara Declaration
David Kaye, UN Rapporteur on Freedom of
Expression
Social media councils
Scandinavian press council model? Or GNI?
Fact (not face) checking
Models all breach freedom of expression & privacy?
Human rights impact assessments

30. Questions
What’s missing?
What evidence is needed?
What should be regulated by platforms?
What should be subject to effective court
oversight (not just theory)?
Does an oversight board or a co-regulator
work better in theory? And practice?

31. Recs 3 + 4. Disinformation best
tackled with digital literacy
But should be regulated with law. Mismatch?
 We cannot all be Finland, with schooling on disinfo
 Most misinformed are dementia generation – over70s
 Media literacy the last refuge of the deregulatory?
4b. in relation to smaller and dual-purpose platforms.
 appropriate for big (US) platforms with fairly tried and tested ToS
 but difficult for the smaller competitors? Innovation defence?
Does a threshold of 2million (5%) or 5million (12%) cut it?
 DE 1 Oct 2017 Netzwerkdurchsetzungsgesetz (Network Enforcement Act NetzDG)
 FR Law 22 Dec 2018 relating to the fight against manipulation of information