Advertisement

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

  • Giovanni ComandèEmail author
Chapter
  • 2.2k Downloads
Part of the Studies in Big Data book series (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.

Keywords

Business Model Personal Data Data Protection Consumer Protection Legal Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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

References

  1. 1.
    European Data Protection Supervisor, Opinion No 4/2015: Towards a new digital ethics: Data, dignity and technology. https://secure.edps.europa.eu/EDPSWEB/webdav/site/mySite/shared/Documents/Consultation/Opinions/2015/15-09-11_Data_Ethics_EN.pdf. Accessed 24 Oct 2016
  2. 2.
    Angwin, J.: The web’s new gold mine: your secrets. Wall Street J. http://online.wsj.com/article/SB10001424052748703940904575395073512989404.html (2010). Accessed 24 Oct 2016
  3. 3.
    Bain & Company: Using Data as a Hidden Asset. http://www.bain.com/publications/articles/using-data-as-a-hidden-asset.aspx (2010). Accessed 24 Oct 2016
  4. 4.
    Pariser, E.: The Filter Bubble. Penguin Press, New York (2011)Google Scholar
  5. 5.
    Tene, O., Polonetsky, J.: Big data for all: privacy and user control in the age of analytics. Northwest. J. Technol. Intellect. Prop. 11(5), 239–273 (2013)Google Scholar
  6. 6.
    Ohm, P.: Response, the underwhelming benefits of big data. Univ. Pa. Law Rev. Online. 161, 339–346 (2013)Google Scholar
  7. 7.
    Van Otterlo, M.: Automated experimentation in Walden 3.0: the next step in profiling, predicting, control and surveillance. Surveill. Soc. 12(2), 255–272 (2014)Google Scholar
  8. 8.
    Lomas, N.: Amazon patents “anticipatory” shipping—to start sending stuff before you’ve bought it. https://techcrunch.com/2014/01/18/amazon-pre-ships/ (2014). Accessed 24 Oct 2016
  9. 9.
    Vaidhyanathan, S.: The Googlization of Everything. University of California Press, Berkeley (2011)Google Scholar
  10. 10.
    Citron, D.K., Pasquale, F.: The scored society: due process for automated predictions. Wash. Law Rev. 89(1), 1–33 (2014)Google Scholar
  11. 11.
    Duhigg, C.: How Companies Learn Your Secrets. The New York Times, New York (2012). http://www.nytimes.com/2012/02/19/magazine/shopping-habits.html. Accessed 24 Oct 2016.
  12. 12.
    Gray, D., Citron, D.K.: The right to quantitative privacy. Minn. Law Rev. 98, 62–144 (2013)Google Scholar
  13. 13.
    Schwartz, P.M.: Privacy and democracy in cyberspace. Vanderbilt Law Rev. 52, 1609–1701 (1999)Google Scholar
  14. 14.
    Schwartz, P.M.: Internet privacy and the state. Conn. Law Rev. 32, 815–859 (2000)Google Scholar
  15. 15.
    Cohen, J.E.: Examined lives: informational privacy and the subject as object. Stanford Law Rev. 52, 1373–1438 (2000)CrossRefGoogle Scholar
  16. 16.
    Cohen, J.E.: Cyberspace as/and space. Columbia Law Rev. 107(1), 210–256 (2007)Google Scholar
  17. 17.
    Solove, D.J.: The Digital Person. New York University Press, New York (2004)Google Scholar
  18. 18.
    Mattioli, D.: On Orbitz, Mac users steered to pricier hotels. Wall Street J.. 23, 2012 (2012). http://www.wsj.com/articles/SB10001424052702304458604577488822667325882. Accessed 24 Oct 2016.
  19. 19.
    Barocas, S., Selbst, A.D.: Big data’s disparate impact. Calif. Law Rev. 104, 671–732 (2016)Google Scholar
  20. 20.
    Colb, S.F.: Innocence, privacy, and targeting in fourth amendment jurisprudence. Columbia Law Rev. 56, 1456–1525 (1996)CrossRefGoogle Scholar
  21. 21.
    Korff, D.: Data protection laws in the EU: the difficulties in meeting the challenges posed by global social and technical developments. In: European Commission Directorate-General Justice, Freedom and Security, Working Paper No. 2. http://ec.europa.eu/justice/policies/privacy/docs/studies/new_privacy_challenges/final_report_working_paper_2_en.pdf (2010). Accessed 24 Oct 2016
  22. 22.
    Zarsky, T.Z.: Governmental data mining and its alternatives. Penn State Law Rev. 116(2), 285–330 (2011)Google Scholar
  23. 23.
    Bamberger, K.A.: Technologies of compliance: risk and regulation in a digital age. Tex. Law Rev. 88(4), 669–739 (2010)Google Scholar
  24. 24.
    Moss, R.D.: Civil rights enforcement in the era of big data: algorithmic discrimination and the computer fraud and abuse act. Columbia Hum. Rights Law Rev. 48(1) (2016).Google Scholar
  25. 25.
    Exec. Office of The President: Big data: seizing opportunities, preserving values. http://www.whitehouse.gov/sites/default/files/docs/big_data_privacy_report_5.1.14_final_print.pdf (2014). Accessed 24 Oct 2016
  26. 26.
    Turow, J.: Niche Envy. MIT Press, Cambridge, MA (2006)Google Scholar
  27. 27.
    Al-Khouri, A.M.: Data ownership: who owns “my data”? Int. J. Manag. Inf. Technol. 2(1), 1–8 (2012)Google Scholar
  28. 28.
    Rajagopal, S.: Customer data clustering using data mining technique. Int. J. Database Manag. Syst. 3(4), 1–11 (2011)Google Scholar
  29. 29.
    Frischmann, B.M., Selinger, E.: Engineering humans with contracts. Cardozo Legal Studies Research Paper No. 493. https://ssrn.com/abstract=2834011 (2016). Accessed 24 Oct 2016
  30. 30.
    Perzanowski, A., Hoofnagle, C.J.: What we buy when we ‘buy now’. Univ. Pa. Law Rev. 165, 317 (2017). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2778072 (forthcoming 2017). Accessed 24 Oct 2014
  31. 31.
    Apple: Terms and Conditions—Game Center. http://www.apple.com/legal/internet-services/itunes/gamecenter/us/terms.html (2013). Accessed 24 Oct 2016
  32. 32.
    Behm, R.: What are the issues? Employment testing: failing to make the grade. http://employmentassessment.blogspot.com/2013/07/what-are-issues.html (2013). Accessed 24 Oct 2016
  33. 33.
    EFF: Timeline of NSA domestic spying. https://www.eff.org/nsa-spying/timeline (2015). Accessed 24 Oct 2016
  34. 34.
    Schneier, B.: Want to evade NSA spying? Don’t connect to the internet. Wired Magazine. http://www.wired.com/opinion/2013/10/149481 (2013). Accessed 24 Oct 2016
  35. 35.
    Rosenbush, S.: Facebook tests software to track your cursor on screen. CIO J. http://blogs.wsj.com/cio/2013/10/30/facebook-considers-vast-increase-in-data-collection (2013). Accessed 24 Oct 2016
  36. 36.
    PrivacySOS: NYPD’s domain awareness system raises privacy, ethics issues. https://privacysos.org/blog/nypds-domain-awareness-system-raises-privacy-ethics-issues/ (2012). Accessed 24 Oct 2016
  37. 37.
    TrapWire: The intelligent security method. http://www.trapwire.com/trapwire.html (2016). Accessed 24 Oct 2016
  38. 38.
    Calo, M.R.: Digital market manipulation. George Wash. Law Rev. 82(4), 95–1051 (2014)Google Scholar
  39. 39.
    Ohm, P.: Broken promises of privacy: responding to the surprising failure of anonymization. UCLA Law Rev. 57, 1701–1777 (2010)Google Scholar
  40. 40.
    Hoofnagle, C.Y.: Big brother’s little helpers: how choicepoint and other commercial data brokers collect and package your data for law enforcement. N. C. J. Int. Law Commer. Regul. 29, 595–637 (2004)Google Scholar
  41. 41.
    Singer, N.: Mapping, and sharing, the consumer genome. The New York Times. http://www.nytimes.com/2012/06/17/technology/acxiom-the-quiet-giant-of-consumer-database-marketing.html (2012). Accessed 24 Oct 2016
  42. 42.
    Tucker, P.: Has big data made anonymity impossible? MIT Technology Review. http://www.technologyreview.com/news/514351/has-big-data-madeanonymity-impossible/ (2013). Accessed 24 Oct 2016
  43. 43.
    Article 29 Data Protection Working Party: Opinion 5/2014 on anonymization techniques. http://ec.europa.eu/justice/data-protection/article-29/documentation/opinion-recommen dation/files/2014/wp216_en.pdf (2014). Accessed 24 Oct 2016Google Scholar
  44. 44.
    Ruggieri, S., Pedreschi, D., Turini, F.: Data mining for discrimination discovery. ACM Trans. Knowl. Discov. Data. 4(2), 1–40 (2010)CrossRefGoogle Scholar
  45. 45.
    Hoofnagle, C.Y., Whittington, J.: “Free”: accounting for the costs of the Internet’s most popular price. UCLA Law Rev. 61, 606–670 (2014)Google Scholar
  46. 46.
    Aziz, A., Telang, R.: What is a digital cookie worth? https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2757325 (2016). Accessed 24 Oct 2016
  47. 47.
    Bozdag, E.: Bias in algorithmic filtering and personalization. Ethics Inf. Technol. 15(3), 209–227 (2013)CrossRefGoogle Scholar
  48. 48.
    Pasquale, F., Citron, D.K.: Promoting innovation while preventing discrimination: policy goals for the scored society. Wash. Law Rev. 89(4), 1413–1424 (2014)Google Scholar
  49. 49.
    Pasquale, F.: Beyond innovation and competition: the need for qualified transparency in Internet intermediaries. Northwest. Univ. Law Rev. 104(1), 105–174 (2010)Google Scholar
  50. 50.
    Pasquale, F.: Restoring transparency to automated authority. J. Telecommun. High Technol. Law. 9, 235–256 (2011)Google Scholar
  51. 51.
    Zarsky, T.Z.: Thinking outside the box: considering transparency, anonymity, and pseudonymity as overall solutions to the problems in information privacy in the Internet society. Univ. Miami Law Rev. 58, 1301–1354 (2004)Google Scholar
  52. 52.
    Zarsky, T.Z.: Transparent predictions. Univ. Ill. Law Rev. 2013(4), 1503–1570 (2013)Google Scholar
  53. 53.
    Cohen, J.E.: Configuring the Networked Self: Law, Code, and the Play of Everyday Practice. Yale University Press, New Haven, CT (2012)Google Scholar
  54. 54.
    Citron, D.K., Gray, D.: Addressing the harm of total surveillance: a reply to professor Neil Richards. Harv. L. Rev. F. 126, 262 (2013)Google Scholar
  55. 55.
    DHS: National network of fusion centers fact sheet. https://www.dhs.gov/national-network-fusion-centers-fact-sheet (2008). Accessed 24 Oct 2016
  56. 56.
    Cohen, J.E.: Privacy, visibility, transparency, and exposure. Univ. Chicago Law Rev. 75(1), 181–201 (2008)Google Scholar
  57. 57.
    Karabenick, S.A., Knapp, J.R.: Effects of computer privacy on help-seeking. J. Appl. Soc. Psychol. 18(6), 461–472 (1988)CrossRefGoogle Scholar
  58. 58.
    Peck, D.: They’re watching you at work. The Atlantic. http://www.osaunion.org/articles/Theyre%20Watching%20You%20At%20Work.pdf (2013). Accessed 24 Oct 2016
  59. 59.
    Citron, D.K.: Data mining for juvenile offenders. Concurring Opinions. http://www.concurringopinions.com/archives/2010/04/data-mining-for-juvenile-offenders.html (2010). Accessed 24 Oct 2016
  60. 60.
    Coleman, E.G.: Coding Freedom. Princeton University Press, Princeton (2013)Google Scholar
  61. 61.
    Marwick, A.E.: How your data are being deeply mined. The New York Review of Books. http://www.nybooks.com/articles/2014/01/09/how-your-data-are-being-deeply-mined/~(2014). Accessed 24 Oct 2016
  62. 62.
    Abdou, H.A., Pointon, J.: Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intell. Syst. Account. Finance Manag. 18(2–3), 59–88 (2011)CrossRefGoogle Scholar
  63. 63.
    Balkin, J.M.: The constitution in the national surveillance state. Minn. Law Rev. 93(1), 1–25 (2008)Google Scholar
  64. 64.
    Kerr, O.S.: Searches and seizures in a digital world. Harv. Law Rev. 119(2), 531–585 (2005)Google Scholar
  65. 65.
    Citron, D.K.: Technological due process. Wash. Univ. Law Rev. 85(6), 1249–1313 (2008)Google Scholar
  66. 66.
    Richards, N.M., King, J.H.: Three paradoxes of big data. Stanford Law Rev. 66, 41–46 (2013)Google Scholar
  67. 67.
    Crawford, K., Schultz, J.: Big data and due process: toward a framework to redress predictive privacy harms. Boston Coll. Law Rev. 55(1), 93–128 (2014)Google Scholar
  68. 68.
    Ramasastry, A.: Lost in translation? Data mining, national security and the “adverse inference” problem. Santa Clara Comput. High Technol. Law J. 22(4), 757–796 (2004)Google Scholar
  69. 69.
    Slobogin, C.: Government data mining and the fourth amendment. Univ. Chicago Law Rev. 75(1), 317–341 (2008)Google Scholar
  70. 70.
    Solove, D.J.: Data mining and the security-liberty debate. Univ. Chicago Law Rev. 75, 343–362 (2008)Google Scholar
  71. 71.
    Solove, D.J.: Privacy and power: computer databases and metaphors for information privacy. Stanford Law Rev. 53(6), 1393–1462 (2001)CrossRefGoogle Scholar
  72. 72.
    Cate, F.H.: Data mining: the need for a legal framework. Harv. Civil Rights Civil Liberties Law Rev. 43, 435 (2008)Google Scholar
  73. 73.
    Strandburg, K.J.: Freedom of association in a networked world: first amendment regulation of relational surveillance. Boston Coll. Law Rev. 49(3), 741–821 (2008)Google Scholar
  74. 74.
    Bloustein, E.J.: Individual and Group Privacy. Transaction Books, New Brunswick, NJ (1978)Google Scholar
  75. 75.
    Conseil National Numerique, Platform Neutrality: Building an open and sustainable digital environment. http://www.cnnumerique.fr/wp-content/uploads/2014/06/PlatformNeutrality_VA.pdf (2014). Accessed 24 Oct 2016
  76. 76.
    Nunez, M.: Senate GOP launches inquiry into Facebook’s news curation. http://gizmodo.com/senate-gop-launches-inquiry-into-facebook-s-news-curati-1775767018 (2016). Accessed 24 Oct 2016
  77. 77.
    Chan, C.: When one app rules them all: the case of WeChat and mobile in China. Andreessen Horowitz. http://a16z.com/2015/08/06/wechat-china-mobile-first/ (2015). Accessed 24 Oct 2016
  78. 78.
    ADL: Google search ranking of hate sites not intentional. http://archive.adl.org/rumors/google_search_rumors.html (2004). Accessed 24 Oct 2016
  79. 79.
    Woan, T.: Searching for an answer: can Google legally manipulate search engine results? Univ. Pa. J. Bus. Law. 16(1), 294–331 (2013)Google Scholar
  80. 80.
    Wu, T.: Machine speech. Univ. Pa. Law Rev. 161, 1495–1533 (2013)Google Scholar
  81. 81.
    Volokh, E., Falk, D.: First amendment protection for~search~engine~search results. http://volokh.com/wp-content/uploads/2012/05/SearchEngineFirstAmendment.pdf~(2012).~Accessed 24 Oct 2016
  82. 82.
    MacKinnon, R.: Consent of the Networked. Basic Books, New York (2012)Google Scholar
  83. 83.
    Chander, A.: Facebookistan. N. C. Law Rev. 90, 1807 (2012)Google Scholar
  84. 84.
    Pasquale, F.: Search, speech, and secrecy: corporate strategies for inverting net neutrality debates. Yale Law and Policy Review. Inter Alia. http://ylpr.yale.edu/inter_alia/search-speech-and-secrecy-corporate-strategies-inverting-net-neutrality-debates (2010). Accessed 24 Oct 2016
  85. 85.
    Richtel, M.: I was discovered by an algorithm. The New York Times. http://archive.indianexpress.com/news/i-was-discovered-by-an-algorithm/1111552/~(2013).~Accessed 24 Oct 2016
  86. 86.
    Slobogin, C.: Privacy at Risk. University of Chicago Press, Chicago (2007)CrossRefGoogle Scholar
  87. 87.
    Zarsky, T.Z.: Understanding discrimination in the scored society. Wash. Law Rev. 89, 1375–1412 (2014)Google Scholar
  88. 88.
    Nissenbaum, H.F.: Privacy in Context. Stanford Law Books, Stanford, CA (2010)Google Scholar
  89. 89.
    Calo, M.R.: The boundaries of privacy harm. Indiana Law J. 86(3), 1131–1162 (2011)Google Scholar
  90. 90.
    Goldman, E.: Data mining and attention consumption. In: Strandburg, K., Raicu, D. (eds.) Privacy and Technologies of Identity. Springer Science + Business Media, New York (2005)Google Scholar
  91. 91.
    Pasquale, F.: The Black Box Society: The Secret Algorithms That Control Money and Information. Harvard University Press, Cambridge, MA (2015)CrossRefGoogle Scholar
  92. 92.
    Clarke, R.: Profiling: a hidden challenge to the regulation of data surveillance. J. Law Inf. Sci. 4(2), 403 (1993)Google Scholar
  93. 93.
    Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Fayyad, U. (ed.) Advances in Knowledge Discovery and Data Mining. AAAI Press, Menlo Park, CA (1996)Google Scholar
  94. 94.
    Paparrizos, J., White, R.W., Horvitz, E.: Screening for pancreatic adenocarcinoma using signals from web search logs: feasibility study and results. J. Oncol. Pract. 12(8), 737–744 (2016)CrossRefGoogle Scholar
  95. 95.
    Friedman, B., Nissenbaum, H.: Bias in computer systems. ACM Trans. Inf. Syst. 14(3), 330–347 (1996). In: Friedman, B. (ed.). Human Values and the Design of Computer Technology. CSLI Publications, Stanford, CA (1997)Google Scholar
  96. 96.
    Hildebrant, M.: Profiling and the rule of law. Identity Inf. Soc. 1(1), 55–70 (2008)CrossRefGoogle Scholar
  97. 97.
    Shkabatur, J.: Cities @ crossroads: digital technology and local democracy in America. Brooklin Law Rev. 76(4), 1413–1485 (2011)Google Scholar
  98. 98.
    Zarsky, T.Z.: “Mine your own business!”: making the case for the implications of the data mining of personal information in the forum of public opinion. Yale J. Law Technol. 5(1), 1–56 (2003)Google Scholar
  99. 99.
    Mayer, J: Tracking the trackers: where everybody knows your username. http://cyberlaw.stanford.edu/node/6740 (2011). Accessed 24 Oct 2016
  100. 100.
    Narayanan, A: There is no such thing as anonymous online tracking. http://cyberlaw.stanford.edu/node/6701 (2011). Accessed 24 Oct 2016
  101. 101.
    Perito, D., Castelluccia, C., Kaafar, M.A., Manilsr, P.: How unique and traceable are usernames? In: Fischer-Hübner, S., Hopper, N. (eds.) Privacy Enhancing Technologies. Springer, Berlin (2011)Google Scholar
  102. 102.
    Datalogix: Privacy policy. https://www.datalogix.com/privacy/ (2016). Accessed 24 Oct 2016
  103. 103.
    Solove, D.J.: Nothing to Hide. Yale University Press, New Haven, CT (2011)Google Scholar
  104. 104.
    Zarsky, T.Z.: Law and online social networks: mapping the challenges and promises of user-generated information flows. Fordham Intell. Prop. Media Entertainment Law J. 18(3), 741–783 (2008)Google Scholar
  105. 105.
    Himma, K.E., Tavani, H.T.: The Handbook of Information and Computer Ethics. Wiley, Hoboken, NJ (2008)CrossRefGoogle Scholar
  106. 106.
    Angwin, J.: Online tracking ramps up—popularity of user-tailored advertising fuels data gathering on browsing habits. Wall Street J. http://www.wsj.com/articles/SB10001424052702303836404577472491637833420 (2012). Accessed 24 Oct 2016
  107. 107.
    World Economic Forum: Rethinking personal data: strengthening trust. http://www3.weforum.org/docs/WEF_IT_RethinkingPersonalData_Report_2012.pdf~(2012). Accessed 24 Oct 2016
  108. 108.
    Posner, R.A.: The economics of privacy. Am. Econ. Rev. 71(2), 405–409 (1981)Google Scholar
  109. 109.
    Calzolari, G., Pavan, A.: On the optimality of privacy in sequential contracting. J. Econ. Theory. 130(1), 168–204 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  110. 110.
    Acquisti, A., Varian, H.R.: Conditioning prices on purchase history. Mark. Sci. 24(3), 367–381 (2005)CrossRefGoogle Scholar
  111. 111.
    Schwartz, P.M.: Property, privacy, and personal data. Harv. Law Rev. 117, 2056–2128 (2003)CrossRefGoogle Scholar
  112. 112.
    Purtova, N.: Property rights in personal data: an European perspective. Dissertation, Uitgeverij BOXPress, Oistervijk (2011)Google Scholar
  113. 113.
    Noam, E.M.: Privacy and self-regulation: markets for electronic privacy. In: Wellbery, B.S. (ed.) Privacy and Self-Regulation in the Information Age. U.S. Dept. of Commerce, National Telecommunications and Information Administration, Washington, D.C. (1997)Google Scholar
  114. 114.
    Cohen, J.E.: Examined lives: informational privacy and the subject as object. Stanford Law Rev. 52, 1373–1437 (1999)CrossRefGoogle Scholar
  115. 115.
    Bergelson, V.: It’s personal but is it mine? Toward property rights in personal information. U.C. Davis Law Review. 37, 379–451 (2003)Google Scholar
  116. 116.
    Laudon, K.C.: Markets and privacy. Commun. ACM. 39(9), 92–104 (1996)CrossRefGoogle Scholar
  117. 117.
    Aperjis, C., Huberman, B.: A market for unbiased private data: paying individuals according to their privacy attitudes. First Monday 17(5) (2012)Google Scholar
  118. 118.
    Kroft, S.: The data brokers: selling your personal information. 60 Minutes. http://www.cbsnews.com/news/data-brokers-selling-personal-information-60-minutes/ (2014). Accessed 24 Oct 2016
  119. 119.
    Jentzsch, N., Preibusch, S., Harasser, A.: Study on monetizing privacy: an economic model for pricing personal information. ENISA Publications. https://www.enisa.europa.eu/publications/monetising-privacy (2012). Accessed 24 Oct 2016
  120. 120.
    Kosner, A.W.: New Facebook policies sell your face and whatever it infers. Forbes. http://www.forbes.com/sites/anthonykosner/2013/08/31/new-facebook-policies-sell-your-faceand-whatever-it-infers/ (2013). Accessed 24 Oct 2016
  121. 121.
    Solove, D.J.: Understanding Privacy. Harvard University Press, Cambridge, MA (2008)Google Scholar
  122. 122.
    Borcea-Pfitzmann, K., Pfitzmann, A., Berg, M.: Privacy 3.0: = data minimization + user control + contextual integrity. Inf. Technol. 53(1), 34–40 (2011)Google Scholar
  123. 123.
    Sweeney, L.: K-anonymity: a model for protecting privacy. Int. J. Uncertain Fuzziness Knowl Based Syst. 10(5), 557–570 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  124. 124.
    Machanavajjhala, A., Kifer, D., Gehrke, J., Venkitasubramaniam, M.: L-diversity: privacy beyond k-anonymity. ACM Trans. Knowl. Discov. Data 1(1), 1–52, Art. 3 (2007)Google Scholar
  125. 125.
    Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: IEEE 23rd International Conference on Data Engineering, pp. 106–115. IEEE, Istanbul (2007)Google Scholar
  126. 126.
    Karjoth, G., Schunter, M., Waidner, M.: Privacy-enabled services for enterprises. http://www.semper.org/sirene/publ/KaSW_02.IBMreport-rz3391.pdf (2002). Accessed 24 Oct 2016
  127. 127.
    Cranor, L.F., Guduru, P., Arjula, M.: User interfaces for privacy agents. ACM Trans. Comput. Hum. Interact. 13(2), 135–178 (2006)CrossRefGoogle Scholar
  128. 128.
    Gritzalis, S.: Enhancing web privacy and anonymity in the digital era. Inf. Manag. Comput. Secur. 12(3), 255–288 (2004)CrossRefGoogle Scholar
  129. 129.
    Andrews, L.: I Know Who You Are and I Saw What You Did: Social Networks and The Death of Privacy. Free Press, New York (2012)Google Scholar
  130. 130.
    Irani, D., Webb, S., Li, K., Pu, C.: Large online social footprints—an emerging threat. http://cobweb.cs.uga.edu/~kangli/src/SecureCom09.pdf (2009). Accessed 24 Oct 2016
  131. 131.
    Irani, D., Webb, S., Pu, C., Li, K.: Modeling unintended personal-information leakage from multiple online social networks. IEEE Internet Comput. 15(3), 13–19 (2011)CrossRefGoogle Scholar
  132. 132.
    Spiekermann, S., Dickinson, I., Günther, O., Reynolds, D.: User agents in e-commerce environments: industry vs. consumer perspectives on data exchange. In: Eder, J., Missikoff, M. (eds.) Advanced Information Systems Engineering. Springer, Berlin (2003)Google Scholar
  133. 133.
    Bott, E.: The do not track standard has crossed into crazy territory. http://www.zdnet.com/the-do-not-track-standard-has-crossed-into-crazy-territory-7000005502/ (2012). Accessed 24 Oct 2016
  134. 134.
    Fujitsu Res. Inst.: Personal data in the cloud: a global survey of consumer attitudes. http://www.fujitsu.com/downloads/SOL/fai/reports/fujitsu_personal-data-in-the-cloud.pdf (2010). Accessed 24 Oct 2016
  135. 135.
    Brunton, F., Nissenbaum, H.: Vernacular resistance to data collection and analysis: a political theory of obfuscation. First Monday. 16(5), 1–16 (2011)CrossRefGoogle Scholar
  136. 136.
    Danezis, G.: Privacy technology options for smart metering. http://research.microsoft.com/enus/projects/privacy_in_metering/privacytechnologyoptionsforsmartmetering.pdf (2011). Accessed 24 Oct 2016
  137. 137.
    Bengtsson, L., Lu, X., Thorson, A., Garfield, R., von Schreeb, J.: Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: a post-earthquake geospatial study in Haiti. PLoS Med. 8(8), e1001083 (2011)CrossRefGoogle Scholar
  138. 138.
    Wesolowski, A., Eagle, N., Tatem, A.J., Smith, D.L., Noor, A.M., Snow, R.W., Buckee, C.O.: Quantifying the impact of human mobility on malaria. Science. 338(6104), 267–270 (2012)CrossRefGoogle Scholar
  139. 139.
    Wesolowski, A., Buckee, C., Bengtsson, L., Wetter, E., Lu, X., Tatem, A.J.: Commentary: containing the ebola outbreak—the potential and challenge of mobile network data. http://currents.plos.org/outbreaks/article/containing-the-ebola-outbreak-the-potential-and-challenge-of-mobile-network-data/ (2014). Accessed 24 Oct 2016
  140. 140.
    Phelps, J., Nowak, G., Ferrell, E.: Privacy concerns and consumer willingness to provide personal information. J. Public Policy Mark. 19(1), 27–41 (2000)CrossRefGoogle Scholar
  141. 141.
    Wood, W., Neal, D.T.: The habitual consumer. J. Consum. Psychol. 19(4), 579–592 (2009)CrossRefGoogle Scholar
  142. 142.
    Buttle, F., Burton, J.: Does service failure influence customer loyalty? J. Consum. Behav. 1(3), 217–227 (2012)CrossRefGoogle Scholar
  143. 143.
    Pew Research Centre: Mobile health 2012. http://www.pewinternet.org/2012/11/08/mobile-health-2012 (2012). Accessed 24 Oct 2016
  144. 144.
    Reinfelder, L., Benenson, Z., Gassmann, F.: Android and iOS users’ differences concerning security and privacy. In: Mackay, W. (ed.) CHI ’13 Extended Abstracts on Human Factors in Computing Systems. ACM, New York, NY (2013)Google Scholar
  145. 145.
    Elkin-Koren, N., Weinstock Netanel, N. (eds.): The Commodification of Information. Kluwer Law International, The Hague (2002)Google Scholar
  146. 146.
    FTC Staff Report: Mobile apps for kids: current privacy disclosures are disappointing. http://www.ftc.gov/os/2012/02/120216mobile_apps_kids.pdf (2012). Accessed 24 Oct 2016
  147. 147.
    FTC Staff Report: Mobile apps for kids: disclosures still not making the grade. http://www.ftc.gov/os/2012/12/121210mobilekidsappreport.pdf (2012). Accessed 24 Oct 2016
  148. 148.
    FTC Staff Report: Mobile privacy disclosures: building trust through transparency. http://www.ftc.gov/os/2013/02/130201mobileprivacyreport.pdf (2013). Accessed 24 Oct 2016
  149. 149.
    Canadian Offices of the Privacy Commissioners: Seizing opportunity: good privacy practices for developing mobile apps. http://www.priv.gc.ca/information/pub/gd_app_201210_e.pdf (2012). Accessed 24 Oct 2016
  150. 150.
    Harris, K.D.: Privacy on the go: recommendations for the mobile ecosystem. http://oag.ca.gov/sites/all/files/pdfs/privacy/privacy_on_the_go.pdf (2013). Accessed 24 Oct 2016
  151. 151.
    GSMA: User perspectives on mobile privacy. http://www.gsma.com/publicpolicy/wpcontent/uploads/2012/03/futuresightuserperspectivesonuserprivacy.pdf (2011). Accessed 24 Oct 2016
  152. 152.
    Sundsøy, P., Bjelland, J., Iqbal, A.M., Pentland, A.S., De Montjoye, Y.A.: Big data-driven marketing: how machine learning outperforms marketers’ gut-feeling. In: Greenberg, A.M., Kennedy, W.G., Bos, N. (eds.) Social Computing, Behavioral-Cultural Modeling and Prediction. Springer, Berlin (2013)Google Scholar
  153. 153.
    Pasquale, F.: Reforming the law of reputation. Loyola Univ. Chicago Law J. 47, 515–539 (2015)Google Scholar
  154. 154.
    Ombelet, P.J., Kuczerawy, A., Valcke, P.: Supervising automated journalists in the newsroom: liability for algorithmically produced news stories. Revue du Droit des Technologies de l’Information. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2768646 (forthcoming 2016). Accessed 24 Oct 2016
  155. 155.
    Latar, N.L., Norsfors, D.: Digital identities and journalism content—how artificial intelligence and journalism may co-develop and why society should care. Innov. Journalism. 6(7), 1–47 (2006)Google Scholar
  156. 156.
    Ombelet, P.J., Morozov, E.: A robot stole my Pulitzer! How~automated journalism and loss of reading privacy may hurt civil~discourse.~http://www.slate.com/articles/technology/future_tense/2012/03/narrative_science_robot_journalists_customized_news_and_the_danger_to_civil_discourse_.single.html (2012). Accessed 24 Oct 2016
  157. 157.
    Hacker, P., Petkova, B.: Reining in the big promise of big data: transparency, inequality, and new regulatory frontiers. Northwest. J. Technol. Intellect. Prop. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2773527 (forthcoming 2016). Accessed 24 Oct 2016
  158. 158.
    Hajian, S., Domingo-Ferrer, J.: Direct and indirect discrimination prevention methods. In: Custers, B., Calders, T., Schermer, B., Zarsky, T. (eds.) Discrimination and Privacy in the Information Society. Springer, New York (2013)Google Scholar
  159. 159.
    Mayer-Schonberger, V., Cukier, K.: Big Data. A Revolution That Will Transform How We Live, Work, And Think. Eamon Dolan/Houghton Mifflin Harcourt, Boston, MA (2014)Google Scholar
  160. 160.
    Calders, T., Verwer, S.: Three naïve Bayes approaches for discrimination-free classification. Data Min. Knowl. Disc. 21(2), 277–292 (2010)CrossRefGoogle Scholar
  161. 161.
    Kamiran, F., Calders, T., Pechenizkiy, M.: Techniques for discrimination-free predictive models. In: Custers, B., Calders, T., Schermer, B., Zarsky, T. (eds.) Discrimination and Privacy in the Information Society. Springer, New York (2013)Google Scholar
  162. 162.
    Tutt, A.: An FDA for algorithms. Adm. Law Rev. 67, 1–26 (2016)Google Scholar
  163. 163.
    FTC: Spring privacy series: alternative scoring products. http://www.ftc.gov/news-events/events-calendar/2014/03/spring-privacy-series-alternative-scoring-products (2014). Accessed 24 Oct 2016
  164. 164.
    Ramirez, E.: The privacy challenges of big data: a view from the lifeguard’s chair. https://www.ftc.gov/public-statements/2013/08/privacy-challenges-big-data-view-lifeguard%E2%80%99s-chair (2013). Accessed 24 Oct 2016
  165. 165.
    Sandvig, C., Hamilton, K., Karahalios, K., Langbort, C.: Auditing algorithms: research methods for detecting discrimination on internet platforms. Data and discrimination: converting critical concerns into productive inquiry. http://www-personal.umich.edu/~csandvig/research/Auditing%20Algorithms%20--%20Sandvig%20--%20ICA%202014%20Data%20and%20Discrimination%20Preconference.pdf (2014). Accessed 24 Oct 2016
  166. 166.
    Benkler, Y.: The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press, New Haven, CT (2006)Google Scholar
  167. 167.
    Citron, D.K.: Open code governance. Univ. Chicago Legal Forum. 2008(1), 355–387 (2008)Google Scholar
  168. 168.
    Barnett, J.M.: The host’s dilemma: strategic forfeiture in platform markets for informational goods. Harv. Law Rev. 124(8), 1861–1938 (2011)Google Scholar
  169. 169.
    Moses, L.: Marketers should take note of when women feel least attractive: what messages to convey and when to send them. ADWEEK. http://www.adweek.com/news/advertising-branding/marketers-should-take-note-when-women-feel-least-attractive-152753 (2013). Accessed 24 Oct 2016
  170. 170.
    Orentlicher, D.: Prescription data mining and the protection of patients’ interests. J. Law Med. Ethics. 38(1), 74–84 (2010)CrossRefGoogle Scholar
  171. 171.
    WPF: Data broker testimony results in new congressional letters to data brokers about vulnerability-based marketing. http://www.worldprivacyforum.org/2014/02/wpfs-data-broker-testimony-results-in-new-congressional-letters-to-data-brokers-regarding-vulnerability-based-marketing/ (2014). Accessed 24 Oct 2016
  172. 172.
    Bakos, Y., Marotta-Wurgler, F., Trossen, D.R.: Does anyone read the fine print? Consumer attention to standard-form contracts. J. Leg. Stud. 43(1), 1–35 (2014)CrossRefGoogle Scholar
  173. 173.
    MacDonald, A.M., Cranor, L.F.: The cost of reading privacy policies. J. Law Policy Inf. Soc. 4(3), 540–565 (2008)Google Scholar
  174. 174.
    Lipford, H.R, Watson, J., Whitney, M., Froiland, K., Reeder, R.W.: Visual vs compact: a comparison of privacy policy interfaces. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 1111–1114 (2010)Google Scholar
  175. 175.
    Passera, S., Haapio, H.: Transforming contracts from legal rules to user-centered communication tools: a human-information interaction challenge. Commun. Des. Q. Rev. 1(3), 38–45 (2013)CrossRefGoogle Scholar
  176. 176.
    Phillips, E.D.: The Software License Unveiled. Oxford University Press, Oxford (2009)Google Scholar
  177. 177.
    Gardner, T.: To read, or not to read… the terms and conditions. The Daily Mail. http://www.dailymail.co.uk/news/article-2118688/PayPalagreement-longer-Hamlet-iTunes-beats-Macbeth.html (2012). Accessed 24 Oct 2016
  178. 178.
    Ayres, I., Schwartz, A.: The no-reading problem in consumer contract law. Stanford Law Rev. 66, 545 (2014)Google Scholar
  179. 179.
    Bar-Gill, O., Ben-Shahar, O.: Regulatory techniques in consumer protection: a critique of European consumer contract law. Common Mark. Law Rev. 50, 109–126 (2013)Google Scholar
  180. 180.
    Luzak, J.: Passive consumers vs. the new online disclosure rules of the consumer rights. Directive. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2553877 (2014). Accessed 24 Oct 2016
  181. 181.
    Luzak, J.: To withdraw or not to withdraw? Evaluation of the mandatory right of withdrawal in consumer distance selling contracts taking into account its behavioral effects on consumers. J. Consum. Policy. 37(1), 91–111 (2014)CrossRefGoogle Scholar
  182. 182.
    Purnhagen, K., Van Herpen, E.: Can bonus packs mislead consumers? An empirical assessment of the ECJ’s mars judgment and its potential impact on EU marketing regulation. In: Wageningen Working Papers Series in Law and Governance 2014/07, http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2503342 (2014)
  183. 183.
    MacDonald, A.M., Reeder, R.W., Kelley, P.G., Cranor, L.F.: A comparative study of online privacy policies and formats. In: Goldberg, I., Atallah, M.J. (eds.) Privacy Enhancing Technologies. Springer, Berlin (2009)Google Scholar
  184. 184.
    Stigler, G.: The Economics of information. J. Polit. Econ. 69(3), 213–225 (1961)CrossRefGoogle Scholar
  185. 185.
    Akerlof, G.A.: The Market for “lemons”: quality uncertainty and the market mechanisms. Q. J. Econ. 84(3), 488 (1970)CrossRefGoogle Scholar
  186. 186.
    Macho-Stadler, I., Pérez-Castrillo, J.D.: An Introduction to the Economics of Information. Oxford University Press, Oxford (2001)Google Scholar
  187. 187.
    Evans, M.B., McBride, A.A., Queen, M., Thayer, A., Spyridakis, J.H.: The effect of style and typography on perceptions of document tone. http://faculty.washington.edu/jansp/Publications/Document_Tone_IEEE_Proceedings_2004.pdf (2004). Accessed 24 Oct 2016
  188. 188.
    Masson, M.E.J., Waldron, M.A.: Comprehension of legal contracts by non-experts: effectiveness of plain language redrafting. Appl. Cogn. Psychol. 8, 67–85 (1994)CrossRefGoogle Scholar
  189. 189.
    Ben-Shahar, O., Schneider, C.E.: More Than You Wanted to Know: The Failure of Mandated Disclosure. Princeton University Press, Princeton (2014)CrossRefGoogle Scholar
  190. 190.
    Radin, M.: Boilerplate. Princeton University Press, Princeton, NJ (2013)CrossRefGoogle Scholar
  191. 191.
    Ben-Shahar, O., Chilton, A.S.: “Best practices” in the design of privacy disclosures: an experimental test. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2670115 (2015). Accessed 24 Oct 2016
  192. 192.
    Miller, A.A.: What do we worry about when we worry about price discrimination? The law and ethics of using personal information for pricing. J. Technol. Law Policy. 19, 41–104 (2014)Google Scholar
  193. 193.
    Mittlestadt, B.D., Allo, P., Taddeo, M., Wachter, S., Floridi, L.: The ethics of algorithms: mapping the debate. Big Data Soc. 1–21 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Scuola Superiore Sant’Anna PisaPisaItaly

Personalised recommendations