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Regulating Algorithms’ Regulation? First Ethico-Legal Principles, Problems, and Opportunities of Algorithms

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Transparent Data Mining for Big and Small Data

Part of the book series: Studies in Big Data ((SBD,volume 32))

Abstract

Algorithms are regularly used for mining data, offering unexplored patterns and deep non-causal analyses in what we term the “classifying society”. In the classifying society individuals are no longer targetable as individuals but are instead selectively addressed for the way in which some clusters of data that they (one or more of their devices) share with a given model fit in to the analytical model itself. This way the classifying society might bypass data protection as we know it. Thus, we argue for a change of paradigm: to consider and regulate anonymities—not only identities—in data protection. This requires a combined regulatory approach that blends together (1) the reinterpretation of existing legal rules in light of the central role of privacy in the classifying society; (2) the promotion of disruptive technologies for disruptive new business models enabling more market control by data subjects over their own data; and, eventually, (3) new rules aiming, among other things, to provide to data generated by individuals some form of property protection similar to that enjoyed by the generation of data and models by businesses (e.g. trade secrets). The blend would be completed by (4) the timely insertion of ethical principles in the very generation of the algorithms sustaining the classifying society.

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Notes

  1. 1.

    “Contrary to some claims, privacy and data protection are a platform for a sustainable and dynamic digital environment, not an obstacle” [1, p. 9].

  2. 2.

    Cambridge Advanced Learner’s Dictionary & Thesaurus of the Cambridge University Press specifies as the first meaning: “to divide things into groups according to their type: The books in the library are classified by/according to subject. Biologists classify animals and plants into different groups”.

  3. 3.

    On the risks and benefits of big data see e.g., Tene and Polonetsky [5]; contra Ohm [6].

  4. 4.

    “Personalization is using more (demographic, but also behavioral) information about a particular individual to tailor predictions to that individual. Examples are Google’s search results based on individual’s cookies or GMail contents” [7, p. 261]. See also https://www.google.com/experimental/gmailfieldtrial.

  5. 5.

    “Algorithms nowadays define how we are seen, by providing a digital lens, tailored by statistics and other biases.” [7, p. 256].

  6. 6.

    Amazon for instance is aiming at shipping goods to us even before we place an order [8]. This approach is very similar to Google attempting to understand what we want before we know we want it. “Google is a system of almost universal surveillance, yet it operates so quietly that at times it’s hard to discern” [9, p. 84].

  7. 7.

    See for more examples Citron and Pasquale [10].

  8. 8.

    By using previous direct interaction, Target knew a teenage girl was pregnant well before her family did [11].

  9. 9.

    See, for instance, the following list of horrors in Gray and Citron [12, p. 81, footnotes omitted]: “Employers have refused to interview or hire individuals based on incorrect or misleading personal information obtained through surveillance technologies. Governmental data-mining systems have flagged innocent individuals as persons of interest, leading to their erroneous classifications as terrorists or security threats. … In one case, Maryland state police exploited their access to fusion centers in order to conduct surveillance of human rights groups, peace activists, and death penalty opponents over a 19 month period. Fifty-three political activists eventually were classified as ‘terrorists,’ including two Catholic nuns and a Democratic candidate for local office. The fusion center subsequently shared these erroneous terrorist classifications with federal drug enforcement, law enforcement databases, and the National Security Administration, all without affording the innocent targets any opportunity to know, much less correct, the record.”

  10. 10.

    On the chilling effect of dataveillance for autonomy and freedom of speech see, for instance, in literature [13,14,15,16,17].

  11. 11.

    The limits of antidiscrimination law to cope with data-driven discrimination have been already highlighted by Barocas and Selbst [19].

  12. 12.

    Some forms of notification at least have already been advocated in the context of the debate surrounding the USA’s Fourth Amendment [20]. In the EU, specific rules on automation are in place [21]. However, some authors claim that automation as such does not require higher scrutiny [22].

  13. 13.

    See also Moss [24], stressing the ability of algorithms to discriminate “in practically and legally analogous ways to a real world real estate agent”.

  14. 14.

    “It is not just the amount of data but also novel ways to analyze this data that change the playing field of any single individual in the information battle against big companies and governments. Data is becoming a key element for profit and control, and computers gain in authority” [7, p. 256; 25].

  15. 15.

    See infra footnotes 73–85 and accompanying text.

  16. 16.

    According to EU Competition Commissioner Margrethe Vestager, the EU Commission is considering the proposal of a specific directive on big data.

  17. 17.

    See in general [26].

  18. 18.

    See also Rajagopal [28].

  19. 19.

    This is the way in which data collection and sharing is supposedly justified in the eyes of customers.

  20. 20.

    For a general description, see Perzanowski [30].

  21. 21.

    Apple [31] for instance imposes the acceptance of the following: “Notwithstanding any other provision of this Agreement, Apple and its licensors reserve the right to change, suspend, remove, or disable access to any products, content, or other materials comprising a part of the Service at any time without notice. In no event will Apple be liable for making these changes. Apple may also impose limits on the use of or access to certain features or portions of the Service, in any case and without notice or liability.”

  22. 22.

    Actually, companies already extensively use algorithms to select employees. For documented cases, see Behm [32].

  23. 23.

    The NSA spying story is nothing new [for the timeline, 33, 34].

  24. 24.

    FaceBook tracks micro-actions such as mouse movements as well [35].

  25. 25.

    See Privacy SOS [36].

  26. 26.

    “TrapWire is a unique, predictive software system designed to detect patterns indicative of terrorist attacks or criminal operations. Utilizing a proprietary, rules-based engine, TrapWire detects, analyzes and alerts on suspicious events as they are collected over periods of time and across multiple locations” [37].

  27. 27.

    See, on the risk of re-identification of anonymized data, Ohm [39].

  28. 28.

    “We are constantly tracked by companies and governments; think of smart energy meters, biometric information on passports, number plate tracking, medical record sharing, etc.” [7, p. 256].

  29. 29.

    Often acting synergistically: see Hoofnagle [40]; Singer [41].

  30. 30.

    See Article 29 Data Protection Working Party [43].

  31. 31.

    Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation, or “GDPR”).

  32. 32.

    See art. 6 of the GDPR on subsequent processing and pseudo-anonymous data.

  33. 33.

    The argument that “free” services actually command a price (in data) for their services and the suggestion that “free users should be treated as consumers for the purposes of consumer protection law” has been already advanced [45, pp. 661–662]; on the economic value of data see Aziz and Telang [46].

  34. 34.

    These algorithms use the model built on other people’s similar behavioral patterns to make suggestions for us if they think we fit the model (i.e. the classification) they have produced [47].

  35. 35.

    As beautifully described by Pasquale and Citron [48, p. 1421]: “Unexplained and unchallengeable, Big Data becomes a star chamber… secrecy is a discriminator’s best friend: unknown unfairness can never be challenged, let alone corrected”. On the importance of transparency and accountability in algorithms of powerful internet intermediaries see also Pasquale [49, 50]. But see, on the role of transparency and the various levels of anonymity, Zarsky [51, 52]; Cohen [53].

  36. 36.

    The point is clearly illustrated by Zarsky [52].

  37. 37.

    In their description [54, pp. 264–265, footnotes omitted]: “Fusion centers access specially designed data-broker data-bases containing dossiers on hundreds of millions of individuals, including their Social Security numbers, property records, car rentals, credit reports, postal and shipping records, utility bills, gaming, insurance claims, social network activity, and drug- and food-store records. Some gather biometric data and utilize facial-recognition software.”

  38. 38.

    See the official description [55].

  39. 39.

    See also Cohen [56]. For the psychological impact of surveillance see Karabenick and Knapp [57].

  40. 40.

    “More unsettling still is the potential combination of surveillance technologies with neuroanalytics to reveal, predict, and manipulate instinctual behavioral patterns of which we are not even aware” [54, p. 265]. Up to the fear that “Based on the technology available, the emergence of a ‘Walden 3.0′ with control using positive reinforcements and behavioral engineering seems a natural development.” [7, p. 265]. Walden 3.0 would be the manifestation of “Walden Two,” the utopian novel written by behavioral psychologist B. F. Skinner (first published in 1948) embracing the proposition that even human behaviour is determined by environmental variables; thus, systematically altering environmental variables can generate a sociocultural system driven by behavioral engineering.

  41. 41.

    See also Citron [59]; Coleman [60]; Marwick [61, p. 22].

  42. 42.

    This phenomenon is particularly problematic for jurists since “[o]ne of the great accomplishments of the legal order was holding the sovereign accountable for decisionmaking and giving subjects basic rights, in breakthroughs stretching from Runnymede to the Glorious Revolution of 1688 to the American Revolution. New algorithmic decisionmakers are sovereign over important aspects of individual lives. If law and due process are absent from this field, we are essentially paving the way to a new feudal order of unaccountable reputational intermediaries” [63, p.19].

  43. 43.

    Government actions have triggered and driven a critical debate. See for instance Ramasastry [68]; Slobogin [69]; Solove [70].

  44. 44.

    On the issue see also Solove [71]; Cate [72]; Strandburg [73].

  45. 45.

    See also Schwartz [13].

  46. 46.

    A serious concern shared both in Europe [75] and in the USA [76], stressing the systematic suppression of conservative news.

  47. 47.

    Of course, it is not only Google that is the problem [77].

  48. 48.

    It was the case in the Google Anti-defamation league [78]; see also Woan [79]; Wu [80].

  49. 49.

    2014 WL 1282730, at 6 (SDNY 2014) (“[A]llowing Plaintiffs to sue Baidu for what are in essence editorial judgments about which political ideas to promote would run afoul of the First Amendment.”).

  50. 50.

    Contra e.g., Case C-131/12.

  51. 51.

    A need already signalled in the literature [82, 83].

  52. 52.

    The high risks of enabling such a free speech approach have been highlighted [84; 7, p. 269].

  53. 53.

    See also Pasquale and Citron [48]; Zarsky [87].

  54. 54.

    According to R. Calo [89] harm must be “unanticipated or, if known to the victim, coerced”.

  55. 55.

    This is the case for both the EU and the USA. See for instance California Online Privacy Protection Act, CAL. BUS & PROF. CODE §§ 22575–22579 (West 2004) (privacy policy requirement for websites on pages where they collect personally identifiable information); CAL. CIV. CODE §§ 1785.11.2, 1798.29, 1798.82 (West 2009); CONN. GEN. STAT. ANN. § 36a-701b (West 2009 & Supp. 2010); GA. CODE ANN. § 10-1-910, 911 (2009).

  56. 56.

    See footnotes 35 and 40 and accompanying text.

  57. 57.

    Mining itself generates new data that change the model and the reading of the clusters.

  58. 58.

    Clarke [92] defines dataveillance as “the systematic use of personal data systems in the investigation or monitoring of the actions or communications of one or more persons”.

  59. 59.

    However, there are several technical definitions of data mining available but they all refer to the discovery of previously unknown, valid patterns and relationships.

  60. 60.

    This is the case for a recent study on pancreatic cancer [94].

  61. 61.

    For an explanation of the actual mechanisms see Solove [70].

  62. 62.

    Meaning agents have an ethical and sometimes legal obligation to answer for their actions, wrongdoing, or mistakes.

  63. 63.

    Transparency is intended as the enabling tool for actual accountability.

  64. 64.

    The different cost-impact of the level of transparency required is analysed by Zarsky [52].

  65. 65.

    Efforts to generate Transparency Enhancing Tools (TETs) is producing an expanding body of research at the crossroad between law and technology [96]. But on the side effects and risks of an excess of transparent information see Shkabatur [97].

  66. 66.

    On this issue see in general Zarsky [22, 98].

  67. 67.

    Literature concentrates on the potential harms of predictive algorithms [67].

  68. 68.

    The “undiscovered observer represents the quintessential privacy harm because of the unfairness of his actions and the asymmetry between his and his victim’s perspective” [89, p. 1160].

  69. 69.

    The literature on the obstacles to obtaining acceptable levels of anonymity on the web is immense [39, 99,100,101]. See also Datalogix [102] privacy policy.

  70. 70.

    Art. 4 GDPR.

  71. 71.

    See the seminal work of Solove [103]; see also Zarsky [104].

  72. 72.

    According to the EU GDPR (art. 4) “pseudonymisation’ means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person”.

  73. 73.

    An average of 56 parties track activities on a website [106]. On the evolution of personal data trade see World Economic Forum [107].

  74. 74.

    “No one can challenge the process of scoring and the results because the algorithms are zealously guarded trade secrets” [10, p. 5]. As illustrated by Richards and King [66, p. 42], “[w]hile Big Data pervasively collects all manner of private information, the operations of Big Data itself are almost entirely shrouded in legal and commercial secrecy”.

  75. 75.

    But see for concerns over propertization Noam [113]; Cohen [114]; Bergelson [115].

  76. 76.

    The role of data aggregation and data brokers is vividly illustrated by Kroft [118].

  77. 77.

    See e.g. Kosner [120].

  78. 78.

    Other authors have already pointed out that one key reading of privacy in the digital age is the lack of choice about the processes that involve us and the impossibility of understanding them [121, p. 133].

  79. 79.

    See now the GDPR; for a technical analysis see Borcea-Pfitzmann et al. [122].

  80. 80.

    See Gritzalis [128]. Indeed several authors have already highlighted the risks to privacy and autonomy posed by the expanding use of social networks: see, for instance the consequent call for a “Social Network Constitution” by Andrews [129] or the proposal principles of network governance by Mackinnon [82] or the worries expressed by Irani et al. [130, 131]; see also Sweeney [123]; Spiekermann et al. [132].

  81. 81.

    See Fujitsu Res. Inst. [134].

  82. 82.

    We are not discussing a science fiction conspiracy to control human beings but the actual side effects of the embrace of specific technological advancements with specific business models and their surrounding legal constraints.

  83. 83.

    This holds true also when the code is verified or programmed by humans with the risk of embedding in it, even unintentionally, the biases of the programmer: “Because human beings program predictive algorithms, their biases and values are embedded into the software’s instructions, known as the source code and predictive algorithms” [10, p. 4].

  84. 84.

    See also Danezis [136].

  85. 85.

    See also Bengtsson et al. [137]; Wesolowski et al. [138, 139].

  86. 86.

    See also Wood and Neal [141]; Buttle and Burton [142]; Pew Research Centre [143]; Reinfelder [144].

  87. 87.

    See also Elkin-Koren and Weinstock [145]; FTC Staff Report [146,147,148]; Canadian Offices of the Privacy Commissioners [149]; Harris [150].

  88. 88.

    See for instance Zhang v. Baidu.com, Inc (2014). But see, e.g., Case C-131/12; Pasquale [153].

  89. 89.

    On the privacy concerns and their social impact see Latar and Norsfors [155]; Ombelet and Morozov [156].

  90. 90.

    The authors also propose: “mandatory active choice between payment with money and payment with data, ex post evaluation of privacy notices, democratized data collection, and wealth or income-responsive fines”. Their proposals enrich an already expanding host of regulatory suggestions. See Hajian and Domingo-Ferrer [158]; Mayer-Schonberger and Cukier [159]; Barocas and Selbst [19]. For a more technical account on fostering discrimination-free classifications, see Calders and Verwer [160]; Kamiran et al. [161]. Recently, the establishment of an ad hoc authority has also been advocated [162]. On market manipulation through the use of predictive and descriptive algorithms see the seminal work of Calo [38].

  91. 91.

    See the EU Directive 2005/29/EC of the European Parliament and of the Council of 11 May 2000, concerning unfair business-to-consumer commercial practices in the internal market.

  92. 92.

    We do not report here those topics that are exclusively related to the USA on which the FTC has authority, such as the relevance of the Fair Credit Reporting Act.

  93. 93.

    For a recent account of algorithms’ disparate impact see Barocas and Selbst [19, p. 671].

  94. 94.

    See an analysis of some techniques potentially available to promote transparency and accountability [165]. See also Moss [24, p. 24], quoting relevant American statutes. Yet if action is not taken at a global level, online auditing can be run in countries where it is not forbidden and results transferred as information in other places. Analogously, a technical attempt to create auditing by using volunteers profiling in a sort of crowdsourcing empowering exercise might even make permissible online auditing in those mentioned jurisdictions forbidding the violation of PPT of web sites by using bots. There is an ongoing debate on this issue. See Benkler [166]; Citron [167]. But see contra Barnett [168]. For a critical analysis urging differentiation of the approach targeting the specific or general public see Zarsky [52].

  95. 95.

    On the potential for discriminatory and other misuses of health data regularly “protected” by professional secrecy see Orentlicher [170].

  96. 96.

    Indeed, it has been estimated that on average we would need 244 h per year to read every privacy policy we encounter [173].

  97. 97.

    Here, app is used as a synonym for software.

  98. 98.

    See also Lipford et al. [174]; Passera and Haapio [175].

  99. 99.

    See Case C–51/94, para 34, holding that consumers who care about ingredients (contained in a sauce) read labels (sic); see also Phillips [176]; Gardner [177]; Ayres and Schwartz [178].

  100. 100.

    E.g. artt. 5, 6 and 10 of Directive 2000/31/EC of the European Parliament and of the Council of 8 June 2000 on certain legal aspects of information society services, in particular electronic commerce, in the Internal Market (‘Directive on electronic commerce’).

  101. 101.

    See also Bar-Gill and Ben-Shahar [179]; Luzak [180, 181]; Purnhagen and Van Herpen [182].

  102. 102.

    See for an information mandate approach: Council Directive 93/13/EEC of 5 April 1993 on unfair terms in consumer contracts; Directive on electronic commerce; Directive 2005/29/EC of the European Parliament and of the Council of 11 May 2005 concerning unfair business-to-consumer commercial practices in the internal market; Directive 2008/48/EC of the European Parliament and of the Council of 23 April 2008 on credit agreements for consumers and repealing Council Directive 87/102/EEC; Directive 2011/83/EU of the European Parliament and of the Council of 25 October 2011 on consumer rights and Regulation (Eu) No 531/2012 of the European Parliament and of the Council of 13 June 2012 on roaming on public mobile communications networks within the Union.

  103. 103.

    See McDonald et al. [183].

  104. 104.

    See in the economics literature: Stigler [184]; Akerlof [185]; Macho-Stadler and Perez-Castrillo [186].

  105. 105.

    On the failure of the disclosure model see in general Ben-Shahar and Schneider [189], Radin [190], and with reference to privacy Ben-Shahar and Chilton [191].

  106. 106.

    The issue of actual market alternatives is not addressed here.

  107. 107.

    See above footnotes 80–81 and accompanying text.

Abbreviations

AI:

Artificial intelligence

CAL. BUS & PROF. CODE:

California business and professions code

CAL. CIV. CODE:

California civil code

CONN. GEN. STAT. ANN.:

Connecticut general statutes annotated

DAS:

Domain awareness system

DHS:

U.S. Department of Homeland Security

DNA:

Deoxyribonucleic acid

EDPS:

European data protection supervisor

EFF:

Electronic Frontier Foundation

EU:

European Union

EU GDPR:

European Union general data protection regulation

EUCJ:

European Union Court of Justice

FTC:

Federal Trade Commission

GA. CODE ANN.:

Code of Georgia annotated

GPS:

Global positioning system

GSM:

Global system for mobile communications

GSMA:

GSM Association

ICT:

Information and communications technology

NSA:

National Security Agency

PETs:

Privacy-enhancing technologies

PPTCs:

Privacy policy terms and conditions

SDNY:

United States District Court for the southern district of New York

ToS:

Terms of service

WEF:

World Economic Forum

WPF:

World Privacy Forum

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Authors and Affiliations

  1. Scuola Superiore Sant’Anna Pisa, Piazza Martiri della Libertà 33, 56127, Pisa, Italy

    Giovanni Comandè

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  1. Giovanni Comandè
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Correspondence to Giovanni Comandè .

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  1. Department of Control and Computer Engineering, Politecnico di Torino, Torino, Italy

    Tania Cerquitelli

  2. Bell Laboratories, Cambridge, United Kingdom

    Daniele Quercia

  3. Carey School of Law, University of Maryland, Baltimore, Maryland, USA

    Frank Pasquale

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Comandè, G. (2017). Regulating Algorithms’ Regulation? First Ethico-Legal Principles, Problems, and Opportunities of Algorithms. In: Cerquitelli, T., Quercia, D., Pasquale, F. (eds) Transparent Data Mining for Big and Small Data. Studies in Big Data, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-319-54024-5_8

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