Slave to the Algo-Rhythms?
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Slave to the Algo-Rhythms?

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Algorithms are taking control of our information rich world. As the twin sibling to Big Data, increasingly they decide how society views us via constructed profiles (as criminals? as terrorists? as ...

Algorithms are taking control of our information rich world. As the twin sibling to Big Data, increasingly they decide how society views us via constructed profiles (as criminals? as terrorists? as rich or poor consumers?); what we see as important, newsworthy, cool or profitable (eg Twitter trending topics, automated stock selling, Amazon recommendations, BBC website top news topics etc); and indeed what we see at all as algorithms are increasingly used to filter our illegal or undesirable content as tools of public policy. Algorithms are peceived by virtue of their automation as neutral, objective and fair, unlike human decision makers - yet evidence increasingly shows the opposite - eg a series of legal complaints assert that Google games its own search results to promote its own economic interests and demote those of competitors or annoyances; while in the defamation field, French, German and Italian courts have decided that algorithmically generated autosuggests in search can be libellous (eg "Bettina Wolf prostitute"). . This paper asks if any legal remedies do or should exist to *audit* proprietary algorithms , given their importance, and asks if one way forward might be via existing and future subject access rights to personal data in EU data protection law. The transformation of these rights as proposed in the draft Data Protection Regulation is not however hopeful.

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  • (Eg, Amazon does not measure “sales rank” of adult books)2. How far can humans interfere, both overtly and in devising/tweaking the algorithms, before not “automated”? What interventions are justified/neutral?
  • punish those not playing ball (eg Italian newspapers refusing to be spidered)

Slave to the Algo-Rhythms? Presentation Transcript

  • 1. Lilian Edwards Professor of E-Governance University of Strathclyde Lilian.edwards@strath.ac.uk @lilianedwards Pangloss: http://blogscript.blogspot.c o.uk/ GikII Seaside Bournemouth, Sep t 2013
  • 2. Slave to the algo-ri(y)thm  Algorithms are Big News  Historical pre-digital notion – everything from knitting patterns to – “a set of logical instructions intended to solve a problem” – often different answers depending on variables. (Braun, #govalgo)
  • 3.  Now –twin sibling to Big Data? – how it is processed to get desired results, recommendations, opinions etc - the “key logic governing the flows of information upon which our society depends” (Tarleton, 2013)- “together data structures and algorithms are two halves of the ontology of the world according to a computer” (Manovich, 1999)  Considerable interest, coders-> business, sociologists, politicians, lawyers; “Governing Algorithms”, MIT, 2013; Mayer-Schoenberger & Cukier Big Data (2013); Morozow To save Everything Click here (2013); Pariser The Filter Bubble (2011)  Has change happened because of better algorithms or more data? Latter due to volume digitised data - M-S p. 35, esp in the Internet industries, eg, search. Some comp sci academics disagree!
  • 4. Why are algorithms important to lawyers & regulators?  Manipulation of personal data to form personal profiles; uses, OBA/targeted ads on social networks, Google etc; , price , service discrimination; criminal/terrorist profiling; -> pre-crime?  Statistical extraction/prediction from data to show what is important/significant/popular/profitable; eg “trending topics” on Twitter; Google News; top Google results on search by keywords; automated stock exchanges; recommendations on Netflix/Amazon etc  Filtering online of unwanted content – passing the buck? Twitter UK anti-women trolling cases summer 2013: ACPO “They [Twitter] are ingenious people, it can't be beyond their wit to stop these crimes”  “Real world” as well as online effects: Algorithms to instruct robots on how to behave adaptively when circumstances change from original programming; driverless cars liability?  Almost hopelessly wide topic! See *Kohl (2013) 12 IJLIT 187.
  • 5. Are algorithms intrinsically fair, neutral or objective?
  • 6. Please please believe me.. Are algorithms “fair”, “neutral”,“objective”? Some key themes  How was the data to which the algorithm is applied selected and made “algorithm-ready”? (“messiness”)  The evaluation of relevance – how “neutral” or “automated” is an algorithm? (exclusion/inclusion; demotion/promotion; manual intervention; competition implications; )  Why is “automated” taken to => “neutral” / “objective”? A game can after all be rigged.. Kohl : “the automation-neutrality platitude”  Do humans remain responsible for automated algorithms then? if so which humans? G. Page Rank is generated by users links, not Google? Editorial responsibility for G News front page?  Legal areas where fairness/neutrality of algorithms becoming an issue: discrimination in profile based advertising (Sweeney); ranking of legal vs illegal download sites on Google; adult content ranking eg not in Amazon top sellers ; unfair competition issues re Google Search results; defamation and autocomplete cases.
  • 7. Enslaving the algorithm: competition cases  Repeated claims Google manipulates search to demote competitors, promote own products  Early US case law : Search King v Google 2003 Google’s rankings not challengeable as “opinion”, 1st Am protected!  However EU competition regulators, national & Commission and FTC in USA have taken allegations more seriously eg Foundem (UK), Ciao (EU), ejustice.fr (Fr) –proposed remedies April 2013 - architectural, labelling remedies – fairly minor.  Can a notion of fairness/neutrality/impartiality be reasonably imposed on Google’s proprietary algorithm?  Is there any canonical form?  It’s Google’s game and they make the rules? But can clearly make or break businesses due to market dominance.  Reliance. Google: “Our users trust our objectivity and no short term goal could ever justify breaching that trust”  Could it ever be “neutral” to suit everyone?
  • 8. Enslaving the algorithm: libel  Algorithmic defamation! Eg. Bettina Wulff case, Germany  Google’s defenses: "The search terms in Google Autocomplete reflect the actual search terms of all users“ (“Crowdsourcing defense”) Also - Automation; objectivity;  If Google’s rankings are only “opinion” , are its autocomplete suggestions not even more so?  But French courts disagree, and some German & Italian .. -> May 2013 German appeal court upheld autocomplete defamation re plaintiff and “Scientology/ fraud” suggestions  “Crowdsourced” defense could inspire astroturfing – Morozow suggests competitors could hire Mechanical Turks..  How difficult would it be for Google to police this given they filter for copyright autosuggest (since 2012)?  Is there social interest in making autocomplete too risky to keep turned on?  Is repressing questionable autocompletes a further version of the filter bubble? (cf L Macalpine/Sally Bercow)
  • 9. The algorithm as black box  Google search algorithm is not just Page Rank (counting links) but c 200 other signals, changed regularly – c 500-600 times/year – some clues given to SEO industry  Why accepted as trade secret?  revenue depends on it – key market advantage?  Secrecy prevents rampant gaming/ “SEO”  ? Disclosure might disrupt the useful claims of automation, neutrality, objectivity  Do we have any rights to audit the algorithm? Should we? Would it help any?  Would it be disastrous for Google to disclose given:  Value comes from the big data not the algorithm?  The algorithm is constantly changed?  Does Google KNOW what its algorithm is doing??  Could DP data subject rights help??
  • 10. Data Protection Directive Art 12: "every data subject [has] the right to obtain from the controller.. - knowledge of the logic involved in any automatic processing of data concerning him at least in the case of the automated decisions referred to in Article 15 (1)“ Art 15(1) : every person has the right "not to be subject to a decision which produces legal effects concerning him or significantly affects him and which is based solely on automated processing of data intended to evaluate certain personal aspects relating to him, such as his performance at work, creditworthiness, reliability, conduct, etc.“ Rec 41: "any person must be able to exercise the right of access to data relating to him which are being processed, in order to verify in particular the accuracy of the data and the lawfulness of the processing“ ..” this right must not adversely affect trade secrets or intellectual property and in particular the copyright protecting the software”
  • 11. Draft DP Regulation (Jan 12)  New Art 15: no mention logic. Right to be told “the significance and envisaged consequences of .. processing [of ones PD] , at least in the case of measures referred to in Article 20”.  Art 20: “Every natural person shall have the right not to be subject to a measure which produces legal effects concerning this natural person or significantly affects this natural person, and which is based solely on automated processing intended to evaluate certain personal aspects relating to this natural person or to analyse or predict in particular the natural person's performance at work, economic situation, location, health, personal preferences, reliability or behaviour.”  *Rec 51: “every data subject should therefore have the right to know and obtain .. what is the logic of the data that are undergoing the processing and what might be, at least when based on profiling, the consequences of such processing. This right should not adversely affect the rights and freedoms of others, including trade secrets or intellectual property…However, the result of these considerations should not be that all information is refused to the data subject Quid iuris! Only LIBE Committee has even mentioned.