The document discusses issues around disinformation and how different actors like platforms, publishers, and public authorities can address problems related to the scale and scope of disinformation. It examines responses from platform, publishers, and policy perspectives. Specifically, it looks at what is known about the scale of disinformation problems and potential actions different actors could take to counter related issues.

1. Disinformation –
Platform, publisher and
policy responses
19 September 2019
Professor Chris Marsden
University of Sussex School of Law

2. With the move to a more digital,
mobile, and platform-dominated
media environment
People increasingly find and access news and information
 via platforms like search engines and social media.
These have empowered citizens in many ways and
 are important drivers of attention to established publishers
 but have also enabled the distribution of disinformation from
a range of different actors.

3. In a context where citizens are
often increasingly sceptical of
 platforms,
 publishers, and
 public authorities,

 what do we know about the scale and scope of disinformation
problems
 and
 what can different actors do to counter the problems we face?

4. Schleswig-Holstein problem
 Three people know the answer
 One’s dead, one’s deranged, and I’ve forgotten…
 AI is an even more difficult issue:

5. But at least it’s not net neutrality…
 Impossible to get right regulatory answer
 Affects largest telecoms companies (and pensions) in every nation
 Actors global and lobbying intense
 Facebook, Google, Twitter, argue they are “special cases”
 Only solutions are co-regulatory and involve “do no evil”
 Can affect electoral politics – especially United States and India
 Perhaps net neutrality and regulating AI have a lot in common!

6. Prior art....

7. Artificial Intelligence (AI) technologies aim to reproduce or surpass
abilities (in computational systems) that would require 'intelligence' if
humans were to perform them. [UK EPSRC]
What is AI? 7

8. Very basic Machine Learning [ML]
already feared in 1933

9. HAL9000?
50 years ago

10. Hong Kong: Telegram, masks v. facial recognition
(and tear gas), no WeChat or Octopus

11. Orwellian problems walking in cities

12. Let’s break it down by actors
 Platforms
 All web hosts but especially
 Facebook/WhatsApp/Instagram,
 Google/YouTube,
 Twitter,
 Microsoft/LinkedIn
 Publishers
 Everybody? Certainly every politician, political party, Twitter user,
 as well as all media, old and new
 Public authorities
 Governments as both regulators and
 Producers of ‘official’ information
 Politicians

13. It’s not just law that regulates us
Regulation by software code
Self-organisation by users
Self-regulation by companies
Co-regulation within markets
Regulation by states

14. LAW
SOCIAL
NORMS
VALUES
MARKET ECONOMICS
ARCHITECTURE:
CODE
Lessig’s Four Spheres of Social Order

15. …and in this it differs from “an artificial intelligence”
It’s a field of study… 15

16. Largely governed through self-regulation
Technology giants appear set to persuade us that
self-regulation remains the only effective route to
legal accountability for machine learning systems,
jeopardising the sustainable introduction of smart
contracts,
permitting algorithmic discrimination and
compromising the implementation of privacy law.

17. Recent public policy focus
AI latest iteration of Machine Learning
But is in fact a very early stage of any defined real AI.
ML is subset of human-computer interaction
(HCI):
1.algorithms
Persia/Greek procedure applied to mathematical rule
2.(big) data
Focus on discrimination that occurs in machine
learning parsed into their interaction

18. Data cleaning is expensive?
 70-80% of cost of AI research is in the data set - outsourced
 GRAHAM, M. & ANWAR, M.A. The global gig economy:
Towards a planetary labour market? First Monday, 2019
 doi:https://doi.org/10.5210/fm.v24i4.9913
 ILO research: http://www.ilo.org/public/libdoc/ilo/2016/490648.pdf

19. 2017-18 Lowlights from AINow

20. AINow set up by Googler Meredith Whittaker
& Microsoft’s Kate Crawford: NYU

21. Discriminatory data is likely to lead
to discriminatory results
Discriminatory algorithms
 as well as those not designed to filter out discrimination
can make those results more discriminatory
Justice requires that lawyers study algorithmic outcomes
in order to ascertain such discrimination,
which may be highly inefficient as well as
outrageous to natural justice and fundamental rights.

22. Public administration has generic
solutions
Administrative law
Natural justice –at least ‘reasonableness’
Right to explanation/remedy?
Discrimination law –
applies to corporate decisions
Specialist technology law
Biomedical/nanotech
Railways, roads, telecoms
Data Protection

24. Or try automated warfare:
DoD regulatory pyramid for Code

25. Focus in this talk is on the private
activities of private companies
 Judges may solve problem in tort/contract
 Only took 100 years in case of railways litigation…
 Would require 1000 technologically savvy judges…
As a result in part of failures of Victorian pre-regulatory period
 we now have
 Anti-discrimination and equality laws
 financial regulation,
 consumer contract law
 Telecommunications regulation etc. etc.

27. Not ethical programming but legal
compliance programming
Really smart contracts
Smart contracts were done TO you, not for you
‘Tap on/off’ Opal/Octopus cards
EULAs and click-wrap licences no-one reads:
Lemley 2000
Freedom of contract online = ‘Freedom of the fox
in the barnyard’?
Changing the terms of trade to consumer law
PROSUMER LAW

28. New Law of Robotics:
Ignorantia juris non excusat
 “Ignorance of the law is no excuse” – Aristotle
 Margetts, Helen and Dunleavy, Patrick (2013) The second wave of digital-era
governance: a quasi-paradigm for government on the Web,
 Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering
Sciences VL - 371 at http://rsta.royalsocietypublishing.org/content/371/1987/20120382.abstract
 Laird, L. (2016) As Governments Open Access to Data, Law Lags Far Behind
 http://www.abajournal.com/news/article/as_governments_open_access_to_data_law_lags_far_behind
 Council of Europe CM/Rec(2014) 6 Recommendation of the Committee of
Ministers to member states on a guide on human rights for Internet users
 Adopted by the Committee of Ministers on 16 April 2014 at the 1197th meeting of
the Ministers' Deputies

29. Prosumer Law:

30. Council of Europe: to err is human,
inducing AI complexity does not absolve

31. Caveat: regulation may not be suitable,
appropriate or feasible for many algorithms
But for those that regulators have most
concern about in
sectors that provide the most sensitive
socioeconomic decisions,
it is a remedy that can be explored.

32. Sensitive public facing sectors?
Banking/Credit, Insurance,
Medical Care & Research,
Social Care,
Policing and Security,
Education,
Transport
AI-piloted Airliners &
Autonomous Vehicles,
Social media
Telecommunications.

33. BTW don’t care (as much) about
big data that’s totally anonymized
Astrophysicists need not fear – geneticists
might….
1. Pseudonymization is not anonymization
2. If you don’t believe that, give me (or
immigration officials) your phone for 2minutes

34. Transparency and replicability are
not the solutions to AI/ML problems
Transparency first requirement of legal recourse
 (though some algorithms can be reverse engineered without
transparency “under the hood” of the machine).
 It is not sufficient, however, for several reasons.
 Claims that the ability to study an algorithm and its operation
 provides a remedy for users who suffer as result of decisions.

35. Things change!
Both the training data and the algorithm itself will change constantly
 e,g. impossible to forecast real time outcomes of Google searches
 vast SEO business attempts approximations without complete accuracy
Remedy that can be achieved is only replicability –
 taking an ‘old’ algorithm and its data at a previous point in time
 to demonstrate whether algorithm and data became discriminatory.
 Estimate just how incomplete a remedy by
 allowing effectively ‘slow motion replays’
 while the game is rushing onwards

36. Bad Place: Real Time Trolley Problem

37. AI regulation and 'ethics washing'
Undertaken by technology companies and their
professional advisors
to persuade policy makers that
self-regulation is the only effective route to legal
accountability for Machine Learning systems,
1. jeopardising the sustainable introduction of
smart contracts,
2. permitting algorithmic discrimination and
3. compromising implementation of data
protection law.

38. Regulators wise to these tricks HT
EU Data Protection Supervisor

39. Ethics washing will fail
Cursory research into
 history of communications regulation and
 Internet law
 demonstrates the falsity of this self-regulation proposition.
See:
 Marsden, C. (2018) “Prosumer Law and Network Platform Regulation: The Long
View Towards Creating Offdata”, 2 Georgetown Tech. L.R. 2, pp.376-398;
 Marsden, C. and T. Meyer (2019) Report for European Parliament: “The effects of
automated content recognition (ACR) technology-based disinformation initiatives on
freedom of expression and media pluralism”

40. Need for systematic redress
by external agency
Ben Wagner (2019) Liable, but Not in Control?
 Ensuring Meaningful Human Agency in Automated Decision-Making
Systems, Policy & Internet, Vol. 11, No. 1, 2019, 104-122 at
https://onlinelibrary.wiley.com/doi/pdf/10.1002/poi3.198
 Self-driving cars,
 police searches using social media/PNR,
 Facebook content moderation

41. Research into ethics washing

42. Research ethics topics
1. Personally identifiable data
EC/95/46 & GDPR equivalents
Council of Europe Convention 108 has 55 signatories
see Greenleaf
Ethics of personal data collection
User informed consent and reuse
2. Proprietary data
The unknown unknowns

43. GDPR is NOT a panacea
“Whereas Google and Facebook in Washington DC faced from 2012 what
turned out to be pretty poor privacy protection via an independent audit of
their new products, the situation in the small towns of Europe was even
worse. Irish regulator has never fined a single company a single €.”
 Chris Marsden and Michael Veale, Gikii 2018, Vienna, 9 September
 Specific Sensitive Data Deregulation (SSDD): the Portarlington-Valetta-Nicosia-
Bucharest Effect in Global Data Protection Law
For contrast, Omer Tene, The Irish DPC is fit: A response to Shaw, May 11,
2018, IAPP, https://iapp.org/news/a/the-irish-dpc-is-fit-a-response-to-shaw/
 “advising controllers and conducting prior consultations is a central pillar of a
DPA’s role under the express language of GDPR (GDPR Articles 36, 51(3)(a)).
 Irish DPC isn’t just to enforce and punish companies GDPR Article 51(1):
 “monitoring the application of this Regulation, in order to protect the
fundamental rights and freedoms of natural persons in relation to processing
and to facilitate the free flow of personal data within the Union”
 “This careful balance— protecting privacy while facilitating data flows —the
cornerstone of the data protection framework since the 1980s.”

44. Competition or comms/media regulation?

45. What can and should be done?
1. Ethical standards for all AI deployed in ‘wild’ – to public
1. ISO standards being formed, basic privacy/human rights impact
assessment
2. No mandated interoperability for public communications providers
– Instant Messaging/Search/Social Media companies
3. APIs opened to dominant (SMP) operators
 Based on Microsoft remedies in longest most expensive antitrust case in
EC history: case started in 1993 in US, EU 1998-2010
 Google case started 2009 – ongoing a decade later
Commission decision of 27 June 2017 Case AT.39740 - Google Search (shopping)

46. 1. Ethical standards for all AI
deployed in ‘wild’ – to public
ISO standards being formed
1. Can be quite powerful influencers c.f. ISO27001 on cybersecurity
2. Typically technical engineering realm not normative standards
3. Embedded in national laws can become weak coregulatory signal
Basic privacy/human rights impact assessment
1. Proposed by UN Rapporteur Prof. David Kaye
2. Also see ‘Regulating Code’ (Brown/Marsden)
3. AI impact assessment suggested by European Data Protection
Supervisor

47. Standards still important!
 Standards Australia chairing ISO Working Party:
 ISO/IEC JTC 1/SC 42 Artificial intelligence
 https://www.iso.org/committee/6794475.html
 Australian Computer Society AI Ethics Committee:
 https://www.acs.org.au/governance/ai-ethics-committee.html
 Data61 (Australian Commonwealth Scientific and Industrial Research
Organisation (CSIRO):
 Dawson D and Schleiger E*, Horton J, McLaughlin J, Robinson C∞, Quezada
G, Scowcroft J, and Hajkowicz S† (2019) Artificial Intelligence: Australia’s Ethics
Framework. Data61 CSIRO, https://data61.csiro.au/en/Our-Work/AI-Framework
 Greenleaf, Graham and Clarke, Roger and Lindsay, David F., (2019)
 Does AI Need Governance? The Potential Roles of a ‘Responsible Innovation
Organisation’ in Australia; Submission to the Human Rights Commissioner on
the White Paper Artificial Intelligence: Governance and Leadership
http://dx.doi.org/10.2139/ssrn.3346149
 UK Information Commissioner’s Office, Feedback request — profiling and
automated decision-making, 6 April 2017,
 https://ico.org.uk/media/about-the-ico/consultations/2013894/ico-feedback-
request-profiling-and-automated-decisionmaking.pdf

48. European Union & OECD
Guidelines widest acceptance
1. 70+ other RRI guidelines and counting
2. US 2019 Executive Order on AI
3. Industry Australia consultation
4. UK Centre for Data Ethics and Innovation (CDEI) at Turing Institute
1. Not perfect, industry sponsored, very light touch

49. Interoperability as an algorithmic
regulatory remedy
Attempt to move beyond glances in the rear view mirror
Silicon Valley mantra is “move fast and break things”
To enforce access to dominant regulated company’s API
 Application Programme Interfaces
Enables brokers, comparator programmes, regulators
to access algorithms in real time & controlled conditions
to observe the algorithm’s behaviour.

50. 2. Interoperability option for
public communications providers
Instant Messaging/Search/Social Media companies
1. Not so radical – required for broadcasters and telcos
1. Electronic Programme Guides
2. Telephone numbering schemes
3. NOT interconnection – up to smaller Ims to decide how to comply
4. Co-regulatory standards
2. Not as utilities but as media providers
1. This is NOT common carrier regulation
2. Not equivalent to energy/postal providers
3. Not as publishers but as printers
1. Arguments on fake news/hate speech for another time
2. Attempts to impose ‘Duty of Care’ fiduciary in UK/US are highly inappropriate

51. MIT Tech Review summarizes

52. US policy consideration?

53. EU Commmissioner Vestager on
interoperability and large platforms
 3 June speech: “Competition and the Digital Economy”
 https://ec.europa.eu/commission/commissioners/2014-
2019/vestager/announcements/competition-and-digital-economy_en
 “Making sure that products made by one company will work
properly with those made by others –
 can be vital to keep markets open for competition.”
Microsoft’s takeover of LinkedIn approval depended on
 agreement to keep Office working properly,
 not just with LinkedIn,
 but also with other professional social networks.
“Commission will need to keep a close eye on strategies that
undermine interoperability”

54. 3. Dominant (SMP) operators
API opened
If dominant –competition and consumer remedy
1. ACCC find dominance by Facebook & Google
2. Only applies to platform aspects of their business
1. i.e. iTunes not Apple phones
Microsoft remedies in longest most expensive
antitrust case in EC history - $5billion fines
1. Case started in 1993 in US, EU 1998-2014
1. Google case started 2009 – ongoing a decade later

55. Note this is not about the
advertising market (only a proxy)

56. Three models – proposed by
Brown/Marsden 2008, 2013
Model 1: Must-carry obligations
broadcasters & Electronic Programme Guides
Model 2: API disclosure requirements
Microsoft from EC rulings
 Case T-201/04, Microsoft v Commission, EU:T:2007:289, 1088
 Decision 24 May 2004 Case C-3/37792 Microsoft; Decision of
16 December 2009 in Case 39530 Microsoft (Tying)
Model 3: Interconnect requirements
Applied to telcos, especially with SMP

57. Interoperability? 3 Types
Protocol interoperability
ability of services/products to interconnect technically
usual interoperability in competition policy
Data interoperability
Recalling Mayer-Schonberger/Cukier
Slice of data to competitors
Full protocol interoperability
What telecoms often thinks of as full
interconnection

58. Why interoperate?
It’s the economics!
Mechanism for achieving any-to-any connectivity –
promotes innovation
There is nothing less valuable than a network with one user!
Interoperability results in increased value of networks
promotes efficient investment in/use of infrastructure
Essential for new entrants to compete with existing
operators on non-discriminatory basis promotes entry

59. Our view of the world – overload?

60. Network 1
Network 2
Two separate networks: bigger
network wins?

61. Network effects of interoperability
 Metcalfe's law states the effect of a
network is proportional to the square
of the number of connected users of
the system
 Network 1 has 6 users = 36
 Network 2 has 4 users = 16
 Network 1 interoperating with Network 2
has 10 users = 100
 The users and operators of each
network gain
Network 1 Network 2
Networks interconnected by specifying
messages that can flow between each
network.
Specification uses an application programming
interface
Basic data flows between the networks
Business rules and value-added information
are not exchanged

62. Social benefits of interoperability too
5 mobile networks that do not interoperate/connect?
forcing all users onto all networks
 if they had the appetite/patience
 Remember when US had different 2.5G standards to EU? CDMA/GSM
 Or winner-takes-all: Facebook!
Mobiles still combine discrete non-interoperable networks
 Skype, WhatsApp, Telegram, Signal, Facebook IM, WeChat
and an interconnected network on the same phone!
 SMS text, phone, Internet

63. Is this remedy more broadly applicable?
 Banking/insurance/medical algorithmic ‘AI’?
 Self-driving vehicles?
 Depends on a variety of socio-economic factors
 Many sectors have regulators working on ‘regulatory sandpit’ solutions
 Interoperability extensively used in sectors with which we are most
familiar

64. Consumer Data Right?
Oz CDR to deliver open banking, open energy and open telecoms?
 Many Europeans – well, we few –very excited about CDR model
 UK Furman Review of Digital Markets: ‘data mobility’
 Competition and Markets Authority: Data, Technology & Analytics unit
 Innovation and Intelligence team: audit algorithms & research tech markets

65. Australian digital regulatory leadership?

66. Questions?

67.  Christopher Kuner, Fred H. Cate, Orla Lynskey, Christopher Millard, Nora Ni Loideain,
and Dan Jerker B. Svantesson, ‘Expanding the artificial intelligence-data protection
debate’ (2018) 8 (4) International Data Privacy Law, 289
 Sandra Wachter, Brent Mittelstadt and Luciano Floridi, ‘Why a Right to Explanation of
Automated Decision-Making Does Not Exist in the General Data Protection Regulation’
(2017) 7 (2) International Data Privacy Law 76;
 Sandra Wachter, Brent Mittelstadt, Chris Russell, ‘Counterfactual Explanations without
Opening the Black Box: Automated Decisions and the GDPR’ (2018) HarvardJL&Tech 1
 Andrew D. Selbst and Julia Powles, ‘Meaningful information and the right to
explanation’ (2017) 7 (4) International Data Privacy Law 233.
 Lilian Edwards, Michael Veale, ‘Slave to the algorithm? Why a ’right to an explanation’
is probably not the remedy you are looking for’ (2017) 16 (1) Duke Law & Technology
Review 18;
 Lilian Edwards, Michael Veale, ‘Enslaving the Algorithm: From a "Right to an
Explanation" to a "Right to Better Decisions”?’ (2018) 16 (3) IEEE Security & Privacy 46

 Lilian Edwards, Michael Veale, ‘Clarity, surprises, and further questions in the Article 29
Working Party draft guidance on automated decision-making and profiling’ (2018) 34 (2)
Computer Law & Security Review 398

68. 10 Steps towards Ethical AI
1. Transparency
 Geeks love this, it’s almost meaningless to average user
2. Explainability
 See above –more useful is replicability
3. Consent
 See GDPR on meaningful & ‘course of business’
4. Discrimination
 Garbage in/Garbage out
5. Accountability to Stakeholders
6. Portability
 Australia’s Consumer Data Right!
7. Redress and Appeal
8. Algorithmic Literacy
 See ‘how to programme your VCR’
9. Independent oversight
10. Governance
 Hosanagar advocates for the creation of an independent Algorithmic
Safety Board, modeled on the Federal Reserve Board
 https://www.vox.com/the-highlight/2019/5/22/18273284/ai-algorithmic-bill-of-
rights-accountability-transparency-consent-bias

69. Economist
policy
leader
6th June