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‘Big Data’ and the Challenge to Informed Consent as a Basis for Privacy Protection Talk for IEEE and CLPC, 22 May 2014, UNSW David Vaile Co-convenor Cyberspace.

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Presentation on theme: "‘Big Data’ and the Challenge to Informed Consent as a Basis for Privacy Protection Talk for IEEE and CLPC, 22 May 2014, UNSW David Vaile Co-convenor Cyberspace."— Presentation transcript:

1 ‘Big Data’ and the Challenge to Informed Consent as a Basis for Privacy Protection Talk for IEEE and CLPC, 22 May 2014, UNSW David Vaile Co-convenor Cyberspace Law and Policy Community Faculty of Law, University of New South Wales

2 Outline About Big Data, ConsentChallenges for consent  Big Data  Distinguishing characteristics  Context  Good and bad consent  Zombie consent  Difficulties with scale  Need for consent rejected?  No purpose, causation?  Manipulation of consent?  Lessons

3 Welcome  I’ll give a talk touching on Consent issues raised by Big Data. It’s first iteration, feedback welcome!  Lyria Bennett Moses will respond, and add observations from her research in technology regulation  Holly Raiche will explain the impact of the recent EU court decision which threw out the Data Retention directive  Questions of fact or clarification are OK in the talk or after, Main discussion at the end?

4 Background: About Big Data, about Consent

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6 What is ‘Big Data’, after the Hype Cycle?  Partly hype and marketing, but real differences beyond scale  Many facets  A Technology, or combination of data and functionality, with certain technical features and characteristics  A ‘Frame’ or brand, a ‘Meme’ with its own rhetorical character and assumptions  Some of the key relevant uses and tools came from marketers with software engineering genius:  Google (core MapReduce tool)  Facebook (now reinventing Big Data data centre hardware)

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9 Big Data as technology: distinguishing features cf. old dBs  PR: Velocity, volume, variety, variability, value  Huge, fast/near real time, heterogeneous…  Omnivorous:  Complete data set, not a sample  Every data set, not just one  All data types, not just obvious records  Adaptable: metadata as well as content  Low integrity: data need not be accurate, current, fit  Purposeless: no need for prior purpose to be designed in  Association not causation

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11 Omnivorous, hungry for data for its own sake?  When “too much data is never enough”?  Many methods based on access to every record in a data set, not a sample or slice  Data takeup is very flexible, so more data sets are low cost to ingest, and thus attractive compared  Can work on both metadata and content data (in comms terms), doing pattern recognition on say movement and photograph

12 Dirty data is OK… until you send in the drones  There are clever means for dealing with both incomplete data and dirty, incorrect data  This is OK for some purposes (weather) but potentially not where an individual is identified and targeted for individual treatment  The less risky end of this is marketing: if dirty data means that some ads are a few % less persuasive than otherwise, little is lost. Key tools and uses came from this industry, or those with no Personal info link.  However, personally serious outcomes such as being refused health insurance at a viable price, or becoming a drone target,

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14 ‘Purposeless’: Outputs not designed in, self-modifying rules?  NOT: collection for a purpose, limited to that purpose, destroyed when purpose is over, stored in a silo secure for that purpose. [Ebay]  Assumption of a ‘fishing expedition’: something will come up, some new association, new insight, cannot pre-specify  ‘We want everything because we want everything…’  Machine learning from any given data set, generates own rules, new associations  Exploitation of new functionality using old collections of data  Prefers longitudinal data retention in lakes to transient silos

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16 Expertise not applicable? (after a certain point)  Is expertise superseded by Big Data systems (if they work)  The scale of data is beyond human comprehension or analysis  The Algorithms similarly: Machine learning rules are written by machines, not programmers, using scale and probabilistic inputs which are beyond our ken  A good Big Data system is iteratively self improving (if you have the feedback correct), so may get better than any expert  At this point the expert’s view of what it is doing may become unreliable, and any possibility of auditing or correction lost  Deus ex Machina? Computer says no? Black box must be obeyed?

17 Context: Ask Forgiveness not Permission  Meme arising from early days of IT: Grace Hopper?  Chips Ahoy magazine, US Navy, July 1986, Chips Ahoy  Appropriate for fast, ‘Agile’, ‘Extreme’ software development to bypass bureaucracy  Assumes truly ‘disposable’, ‘throwaway’ prototypes. Fixed by v2  Also works for innovative business models, where failure is OK, test limits  FAIL: for personally significant information: v2 does not help the victim of unintended disclosure, publication or exposure  FAIL: if there is no effective enforcement (FB wrist slap 2011)

18 Context: ‘Cult of Disruption’ in key data driven firms  ‘Forgiveness not permission’ (Google, many others)  ‘Move fast and break things’ (FB, fudged last week)  Attractive to small start-ups and to the online giants  Often implies the key disruption is cool new technology  But often also relies on traditional risk-shifting, cost-evading and side- stepping obligations  Reluctance to accept obligations re Tax (Google, Apple…), Insurance (Uber), Wages, License fees, Compliance, etc.  Essentially inimical to idea of compliance

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20 Consent  One legal basis for data processing is “freely-given, unambiguous and informed consent of the data subject to the specific processing operation.”  Article 2 (h), EU Data Protection Directive  Consent also works as the basis for entry into a contract  Consumer protection recognises contract law is often unfair to consumers because of gross disparity of knowledge and bargaining power with a big business  Precautionary Principle: if there is compelling info to suggest a path has an irrevocable step into a situation with real risk of serious harm, don’t proceed until you can clarify the risk and know it is OK.

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22 Good and bad consent (thinking as a subject)  Informed, not ignorant (info suits your needs)  Unbundled, not bundled (holding you hostage to something essential, all or nothing)  Before the fact, not after  Explicit not implied  Revocable not permanent – this is your insurance (Google likes to think you get a chance to say yes, until you do, and no way back)

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24 Consent needs proper information to reveal the ‘price’  A business assesses ‘cost and risk’ against benefit  Due diligence needs specific info to to work out who to trust  You need info to help you appreciate risks, not just benefits, and assess probability and impact  Different people at different times need diff. info - Not beyond the power of Big Data firms  Potential reluctance to be specific, across the board: Information asymmetry: they know you, but not the reverse

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26 Zombie consent: click that nice blue button  Many consumers, given little real choice, bundled consent, confusing and meaningless info just click the online consent button  Trained like rats or birds to click the button to get the reward  It says: “I have read and understood and agree”  It means: “ I haven’t read and couldn’t understand, whatever”  The role of consent may be limited by both consumer behaviour (lying about their agreement) and the complicity of operators (who could offer

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28 Recent developments  US: two reports to Obama – minimal consideration of consent  EU  ECJ ruling invalidating data protection directive - Holly  EDSP report, Privacy and competitiveness in the age of big data, March 2014  ECJ ruling requiring Google to offer a ‘right to be forgotten’, Spanish bankruptcy – spent convictions model – revocation?

29 Challenges for Big Data and Consent

30 Vast aggregations are difficult to explain for consent purposes  The complexity and extent of the functionality may present issues, especially if there is no constraint on use or purpose  But it could be done… If it mattered  Google is a master of translating complexity to comprehensible chunks  Data visualisation could help, key big data tool  Conscious decision not to try, to seek obfuscation?  Reluctance to accept transparency? Hiding behind complexity

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32 Claims it’s too hard, Privacy is over, Consent irrelevant  From of the cult of Disruption: We are new, fast, smart, cool, so just get out of the way!  Respecting your wishes would cramp our style, so don’t make us ask  (Real issue: we don’t want to have to obey a refusal to consent)  Bundled consent: if we have to ask consent, ‘the terrorists will win’, or ‘you won’t have any friends’, or ‘no new toys’  Is this a real objection, or framing the question to get No?  Potential reluctance to learn from e-commerce, micro-transactions, Bitcoin, other new technologies, or even Big Data itself?

33 Consent v. Unequal bargaining power of Big Data ops  Have we stepped back into a contract-first world, before consumer protection stepped in to redress the imbalances?  Unilateral, non-negotiable, incomplete contracts  Swedish Data Protection Board 2013: Google refuses to negotiate on a contract that omits key data about who, where and for what purposes your personal info can be used  Compliance impossible to ascertain  So: Not suitable to sign!  The absence of key information is presented as a bluff. The Swedes called it, everyone else takes the sucker’s option  Role for consumer protection law to redress the balance?

34 ‘Forgiveness, not permission’ = No consent?  The ‘forgiveness’ slogan appears to be fundamentally hostile in principle to idea that the data subject might have the prior right over what is done with their data – possession?  Conflates external regulation with personal permission and consent  Permission in this case is permission from the individual in the form of informed consent  Forgiveness often is sought from other than the affected subject, or only sought if caught  Hostility to any form of prior permission seeking?  When consent is reluctantly sought, it is formalistic not aimed at enabling due diligence or real understanding

35 Association not Causation: should you ever consent to this?  Association, uncertainty, incompleteness, out of dateness, inapplicability to the purpose may all not be fatal flaws for the original task of marketing tweaks  But as soon as real decisions and risks are linked to individual, the reality that possibly random associations are at the core,  not falsifiable evidence-based understanding of a true deep causal connection  Raises questions about whether anyone should be expected to accept this level of uncertainty  Especially when the means for auditing or verification or correction are absent

36 No purpose = No information for consent?  OECD Principles-based Privacy law is based on permitting any reasonable use of your personal data, not getting in the way of specific necessary tasks  But it assumes you must be told the purpose for a collection, use and/or disclosure  This is so you understand what it’s for, and can decline if you are not happy with that purpose or use (even at some cost)  Search warrants are also issued for a specific purpose, and not for ‘fishing expedition’  Big data purposes are often made up as you go along, precisely a ‘fishing expedition’ with machine learning and new associations, re-identification, new algorithms

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38 Consent v. Deep understanding of what’s in your head  Psychographic profiling aims to ‘get inside your head’ by extracting insights from associations from data surveillance  A/B testing and other techniques used to refine understanding of all the factors which affect choice to clicking ‘yes’  Capacity to understand you, predict your behaviour or reactions  Capacity to persuade you, find neuro-linguistic keys to you  Capacity to frame a message irresistible to YOU  Flies under the radar, like subliminal advertising (illegal manipulation)  Potentially undermines basis for real consent?

39 Lessons  Too early to tell - real challenges for consent from Big Data?  Some may arise from the technology, or the business model  But some from the old-fashioned ‘cult of disruption’: uses technology to distract from unwillingness to meet obligations  Awareness of implications and risks is hard  There is a reluctance to assist understanding of this: denial, obfuscation, missing info, incomplete contracts  A poor basis for consumer friendly negotiation?  A poor basis for trust?

40 Questions? David Vaile Cyberspace Law and Policy Community Faculty of Law, UNSW


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