Marsden Disinformation Algorithms #IGF2019

Professor of Law at University of Sussex um
29. Nov 2019

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Marsden Disinformation Algorithms #IGF2019

  1. Regulating Disinformation with Artificial Intelligence (AI) Prof. Chris Marsden (Sussex) & Dr Trisha Meyer (IES, Free University of Brussels VUB) United Nations Internet Governance Forum 29 November 2019
  2. 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.
  3. 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
  4. 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.
  5. 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
  6. Fake news?
  7. 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
  8. Gigantic wave of Brexit ads in last week (electioneering suspended)
  9. Result
  10. FBK/Cambridge Analytica via N.Ireland
  11. 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, 2017, Danish Institute for International Studies  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  Marsden, C. [2004] Hyperglobalized Individuals: the Internet, globalization, freedom and terrorism 6 Foresight 3 at 128-140
  12. Neil Mohan made Jordan Peterson & Yaxley-Lennon famous  “Google Paid This Man $100 Million: Here's His Story”  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”
  13. 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.
  14. 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
  15. 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, moderation-works
  16. 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, content-governance-and-enforcement/10156443129621634/
  17. 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
  18. 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
  19. Different aspects of disinformation merit different types of regulation All proposed policy solutions stress the importance of literacy and cybersecurity
  20. 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
  21. 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.
  22. 2. Advise against regulatory action to encourage increased use of AI for content moderation (CoMo) purposes, without strong independent human review and appeal processes.
  23. 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.
  24. 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.
  25. 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.
  26. Specific Policy Options: The Co-Regulatory Triangle
  27. 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,
  28. Prior art....
  29. Questions?