The Tyranny of Data? The Bright and Dark Sides of Data-Driven Decision-Making for Social Good

  • Bruno LepriEmail author
  • Jacopo Staiano
  • David Sangokoya
  • Emmanuel Letouzé
  • Nuria Oliver
Part of the Studies in Big Data book series (SBD, volume 32)


The unprecedented availability of large-scale human behavioral data is profoundly changing the world we live in. Researchers, companies, governments, financial institutions, non-governmental organizations and also citizen groups are actively experimenting, innovating and adapting algorithmic decision-making tools to understand global patterns of human behavior and provide decision support to tackle problems of societal importance. In this chapter, we focus our attention on social good decision-making algorithms, that is algorithms strongly influencing decision-making and resource optimization of public goods, such as public health, safety, access to finance and fair employment. Through an analysis of specific use cases and approaches, we highlight both the positive opportunities that are created through data-driven algorithmic decision-making, and the potential negative consequences that practitioners should be aware of and address in order to truly realize the potential of this emergent field. We elaborate on the need for these algorithms to provide transparency and accountability, preserve privacy and be tested and evaluated in context, by means of living lab approaches involving citizens. Finally, we turn to the requirements which would make it possible to leverage the predictive power of data-driven human behavior analysis while ensuring transparency, accountability, and civic participation.


Information Asymmetry Machine Learning Model Credit Score Computational Thinking Intended Beneficiary 
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.


  1. 1.
    Akerlof, G.A.: The market for “lemons”: quality uncertainty and the market mechanism. Q. J. Econ. 84 (3), 488–500 (1970)CrossRefGoogle Scholar
  2. 2.
    Akerlof, G.A., Shiller, R.J.: Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism. Princeton University Press, Princeton (2009)Google Scholar
  3. 3.
    Barocas, S., Selbst, A.D.: Big data’s disparate impact. Calif. Law Rev. 104, 671–732 (2016)Google Scholar
  4. 4.
    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
  5. 5.
    Benkler, Y.: The Wealth of Networks. Yale University Press, New Haven (2006)Google Scholar
  6. 6.
    Berendt, B., Preibusch, S.: Better decision support through exploratory discrimination-aware data mining: foundations and empirical evidence. Artif. Intell. Law 22 (2), 1572–8382 (2014)CrossRefGoogle Scholar
  7. 7.
    Blondel, V.D., Decuyper, A., Krings, G.: A survey of results on mobile phone datasets analysis. EPJ Data Sci. 4 (10) (2015)Google Scholar
  8. 8.
    Blumenstock, J., Cadamuro, G., On, R.: Predicting poverty and wealth from mobile phone metadata. Science 350 (6264), 1073–1076 (2015)CrossRefGoogle Scholar
  9. 9.
    Bogomolov, A., Lepri, B., Ferron, M., Pianesi, F., Pentland, A.: Daily stress recognition from mobile phone data, weather conditions and individual traits. In: Proceedings of the 22nd ACM International Conference on Multimedia, pp. 477–486 (2014)Google Scholar
  10. 10.
    Bogomolov, A., Lepri, B., Staiano, J., Oliver, N., Pianesi, F., Pentland, A.: Once upon a crime: towards crime prediction from demographics and mobile data. In: Proceedings of the International Conference on Multimodal Interaction (ICMI), pp. 427–434 (2014)Google Scholar
  11. 11.
    Bogomolov, A., Lepri, B., Staiano, J., Letouzé, E., Oliver, N., Pianesi, F., Pentland, A.: Moves on the street: classifying crime hotspots using aggregated anonymized data on people dynamics. Big Data 3 (3), 148–158 (2015)CrossRefGoogle Scholar
  12. 12.
    Burrell, J.: How the machine ‘thinks’: understanding opacity in machine learning algorithms. Big Data Soc. 3 (1) (2016)Google Scholar
  13. 13.
    Calders, T., Verwer, S.: Three naive Bayes approaches for discrimination-free classification. Data Min. Knowl. Disc. 21 (2), 277–292 (2010)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Calders, T., Zliobaite, I.: Why unbiased computational processes can lead to discriminative decision procedures. In: Custers, B., Calders, T., Schermer, B., Zarsky, T. (eds.) Discrimination and Privacy in the Information Society, pp. 43–57. Springer, Berlin (2013)CrossRefGoogle Scholar
  15. 15.
    Centellegher, S., De Nadai, M., Caraviello, M., Leonardi, C., Vescovi, M., Ramadian, Y., Oliver, N., Pianesi, F., Pentland, A., Antonelli, F., Lepri, B.: The mobile territorial lab: a multilayered and dynamic view on parents’ daily lives. EPJ Data Sci. 5 (3) (2016)Google Scholar
  16. 16.
    Chainey, S.P., Tompson, L., Uhlig, S.: The utility of hotspot mapping for predicting spatial patterns of crime. Secur. J. 21, 4–28 (2008)CrossRefGoogle Scholar
  17. 17.
    Christin, A., Rosenblatt, A., boyd, d.: Courts and predictive algorithms. Data Civil Rights Primer (2015)Google Scholar
  18. 18.
    Citron, D.K., Pasquale, F.: The scored society. Wash. Law Rev. 89 (1), 1–33 (2014)Google Scholar
  19. 19.
    Crawford, K., Schultz, J.: Big data and due process: toward a framework to redress predictive privacy harms. Boston College Law Rev. 55 (1), 93–128 (2014).Google Scholar
  20. 20.
    De Choudhury, M., Gamon, M., Counts, S., Horvitz, E.: Predicting depression via social media. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (2013)Google Scholar
  21. 21.
    De Filippi, P.: The interplay between decentralization and privacy: the case of blockchain technologies. J. Peer Production 7 (2015)Google Scholar
  22. 22.
    de Montjoye, Y.-A., Hidalgo, C., Verleysen, M., Blondel, V.: Unique in the crowd: the privacy bounds of human mobility. Sci. Rep. 3 (2013)Google Scholar
  23. 23.
    de Montjoye, Y.-A., Shmueli, E., Wang, S., Pentland, A.: OpenPDS: protecting the privacy of metadata through safeanswers. PLoS One 9 (7), e98790 (2014)CrossRefGoogle Scholar
  24. 24.
    de Montjoye, Y.-A., Radaelli, L., Singh, V.K., Pentland, A.: Unique in the shopping mall: on the re-identifiability of credit card metadata. Science 347 (6221), 536–539 (2015)Google Scholar
  25. 25.
    de Oliveira, R., Karatzoglou, A., Concejero Cerezo, P., Armenta Lopez de Vicuña, A., Oliver, N.: Towards a psychographic user model from mobile phone usage. In: CHI’11 Extended Abstracts on Human Factors in Computing Systems, pp. 2191–2196. ACM, New York (2011)Google Scholar
  26. 26.
    Devarajan, S.: Africa’s statistical tragedy. Rev. Income Wealth 59 (S1), S9–S15 (2013)CrossRefGoogle Scholar
  27. 27.
    Diakopoulos, N.: Algorithmic accountability: journalistic investigation of computational power structures. Digit. Journal. 3 (3), 398–415 (2015)CrossRefGoogle Scholar
  28. 28.
    Easterly, W.: The Tyranny of Experts. Basic Books, New York (2014)Google Scholar
  29. 29.
    Eck, J., Chainey, S., Cameron, J., Wilson, R.: Mapping crime: understanding hotspots. National Institute of Justice, Washington (2005)Google Scholar
  30. 30.
    Faurholt-Jepsena, M., Frostb, M., Vinberga, M., Christensena, E.M., Bardram, J.E., Kessinga, L.V.: Smartphone data as objective measures of bipolar disorder symptoms. Psychiatry Res. 217, 124–127 (2014)CrossRefGoogle Scholar
  31. 31.
    Feldman, M., Friedler, S.A., Moeller, J., Scheidegger, C., Venkatasubramanian, S.: Certifying and removing disparate impact. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 259–268 (2015)Google Scholar
  32. 32.
    Ferguson, A.G.: Crime mapping and the fourth amendment: redrawing high-crime areas. Hastings Law J. 63, 179–232 (2012)Google Scholar
  33. 33.
    Fields, G.: Changes in poverty and inequality. World Bank Res. Obs. 4, 167–186 (1989)CrossRefGoogle Scholar
  34. 34.
    Fiske, S.T.: Stereotyping, prejudice, and discrimination. In: Gilbert, D.T., Fiske, S.T., Lindzey, G. (eds.) Handbook of Social Psychology, pp. 357–411. McGraw-Hill, Boston (1998)Google Scholar
  35. 35.
    Frias-Martinez, E., Williamson, G., Frias-Martinez, V.: An agent-based model of epidemic spread using human mobility and social network information. In: 2011 International Conference on Social Computing (SocialCom), pp. 57–64. IEEE, New York (2011)Google Scholar
  36. 36.
    Gillespie, T.: The relevance of algorithms. In: Gillespie, T., Boczkowski, P., Foot, K. (eds.) Media Technologies: Essays on Communication, Materiality, and Society, pp. 167–193. MIT Press, Cambridge (2014)Google Scholar
  37. 37.
    Ginsberg, J., Mohebbi, M.H., Patel, R.S., Brammer, L., Smolinski, M.S., Brilliant, L.: Detecting influenza epidemics using search engine query data. Nature 457, 1012–1014 (2009)CrossRefGoogle Scholar
  38. 38.
    Hajian, S., Bonchi, F., Castillo, C.: Algorithmic bias: from discrimination discovery to fairness-aware data mining. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2125–2126. ACM, New York (2016)Google Scholar
  39. 39.
    Jean, N., Burke, M., Xie, M., Davis, W.M., Lobell, D.B., Ermon, S.: Combining satellite imagery and machine learning to predict poverty. Science 353 (6301), 790–794 (2016)CrossRefGoogle Scholar
  40. 40.
    Jerven, M.: Poor Numbers: How We Are Misled by African Development Statistics and What to Do About It. Cornell University Press, Ithaca (2013)Google Scholar
  41. 41.
    King, G.: Ensuring the data-rich future of the social sciences. Science 331 (6018), 719–721 (2011)CrossRefGoogle Scholar
  42. 42.
    Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. 110 (15), 5802–5805 (2013)CrossRefGoogle Scholar
  43. 43.
    Kuznets, S.: Economic growth and income inequality. Am. Econ. Rev. 45, 1–28 (1955)Google Scholar
  44. 44.
    Latzer, M., Hollnbuchner, K., Just, N., Saurwein, F.: The economics of algorithmic selection on the internet. In: Bauer, J., Latzer, M. (eds.) Handbook on the Economics of the Internet. Edward Elgar, Cheltenham (2015)Google Scholar
  45. 45.
    Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabasi, A-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., Jebara, T., King, G., Macy, M., Roy, D., Van Alstyne, M.: Computational social science. Science 323 (5915), 721–723 (2009)CrossRefGoogle Scholar
  46. 46.
    Lepri, B., Staiano, J., Shmueli, E., Pianesi, F., Pentland, A.: The role of personality in shaping social networks and mediating behavioral change. User Model. User-Adap. Inter. 26 (2), 143–175 (2016)CrossRefGoogle Scholar
  47. 47.
    LiKamWa, R., Liu, Y., Lane, N.D., Zhong, L.: Moodscope: building a mood sensor from smartphone usage patterns. In: Proceedings of the 11th Annual International Conference on Mobile Systems, Applications, and Service (MobiSys), pp. 389–402 (2013)Google Scholar
  48. 48.
    Liu, H.Y., Skjetne, E., Kobernus, M.: Mobile phone tracking: in support of modelling traffic-related air pollution contribution to individual exposure and its implications for public health impact assessment. Environ. Health 12, 93 (2013)CrossRefGoogle Scholar
  49. 49.
    Louail, T., Lenormand, M., Cantu Ros, O.G., Picornell, M., Herranz, R., Frias-Martinez, E., Ramasco, J.J., Barthelemy, M.: From mobile phone data to the spatial structure of cities. Sci. Rep. 4, 5276 (2014)CrossRefGoogle Scholar
  50. 50.
    Lu, X., Bengtsson, L., Holme, P.: Predictability of population displacement after the 2010 haiti earthquake. Proc. Natl. Acad. Sci. 109, 11576–11581 (2012)CrossRefGoogle Scholar
  51. 51.
    Major, B., O’Brien, L.T.: The social psychology of stigma. Annu. Rev. Psychol. 56, 393–421 (2005)CrossRefGoogle Scholar
  52. 52.
    Matic, A., Oliver, N.: The untapped opportunity of mobile network data for mental health. In: Future of Pervasive Health Workshop, vol. 6. ACM, New York (2016)Google Scholar
  53. 53.
    Mohler, G.O., Short, M.B., Brantingham, P.J., Schoenberg, F.P., Tita, G.E.: Self-exciting point process modeling of crime. J. Am. Stat. Assoc. 106, 100–108 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  54. 54.
    Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. Technical Report, Kent University (2009)Google Scholar
  55. 55.
    Ofli, F., Meier, P., Imran, M., Castillo, C., Tuia, D., Rey, N., Briant, J., Millet, P., Reinhard, F., Parkan, M., Joost, S.: Combining human computing and machine learning to make sense of big (aerial) data for disaster response. Big Data 4, 47–59 (2016)CrossRefGoogle Scholar
  56. 56.
    Ohm, P.: Broken promises of privacy: responding to the surprising failure of anonymization. UCLA Law Rev. 57, 1701–1777 (2010)Google Scholar
  57. 57.
    Oliver, N., Matic, A., Frias-Martinez, E.: Mobile network data for public health: opportunities and challenges. Front. Public Health 3, 189 (2015)CrossRefGoogle Scholar
  58. 58.
    O’Neil, C.: Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown, New York (2016).Google Scholar
  59. 59.
    Osmani, V., Gruenerbl, A., Bahle, G., Lukowicz, P., Haring, C., Mayora, O.: Smartphones in mental health: detecting depressive and manic episodes. IEEE Pervasive Comput. 14 (3), 10–13 (2015)CrossRefGoogle Scholar
  60. 60.
    Pager, D., Shepherd, H.: The sociology of discrimination: racial discrimination in employment, housing, credit and consumer market. Annu. Rev. Sociol. 34, 181–209 (2008)CrossRefGoogle Scholar
  61. 61.
    Pasquale, F.: The Black Blox Society: The Secret algorithms That Control Money and Information. Harvard University Press, Cambridge (2015)CrossRefGoogle Scholar
  62. 62.
    Pastor-Escuredo, D., Torres Fernandez, Y., Bauer, J.M., Wadhwa, A., Castro-Correa, C., Romanoff, L., Lee, J.G., Rutherford, A., Frias-Martinez, V., Oliver, N., Frias-Martinez, E., Luengo-Oroz, M.: Flooding through the lens of mobile phone activity. In: IEEE Global Humanitarian Technology Conference, GHTC’14. IEEE, New York (2014)Google Scholar
  63. 63.
    Pentland, A.: Society’s nervous system: building effective government, energy, and public health systems. IEEE Comput. 45 (1), 31–38 (2012)CrossRefGoogle Scholar
  64. 64.
    Perry, W.L., McInnis, B., Price, C.C., Smith, S.C., Hollywood, J.S.: Predictive Policing: The Role of Crime Forecasting in Law Enforcement Operations. Rand Corporation, Santa Monica (2013)Google Scholar
  65. 65.
    Podesta, J., Pritzker, P., Moniz, E.J., Holdren, J., Zients, J.: Big data: seizing opportunities, preserving values. Technical Report, Executive Office of the President (2014)Google Scholar
  66. 66.
    Ramirez, E., Brill, J., Ohlhausen, M.K., McSweeny, T.: Big data: a tool for inclusion or exclusion? Technical Report, Federal Trade Commission, January 2016Google Scholar
  67. 67.
    Ratcliffe, J.H.: A temporal constraint theory to explain opportunity-based spatial offending patterns. J. Res. Crime Delinq. 43 (3), 261–291 (2006)CrossRefGoogle Scholar
  68. 68.
    Ravallion, M.: The economics of poverty: history, measurement, and policy. Oxford University Press, Oxford (2016)CrossRefGoogle Scholar
  69. 69.
    Ribeiro, M.T., Singh, S., Guestrin, C.: “why should I trust you?”: explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, August 13–17, 2016, pp. 1135–1144 (2016)Google Scholar
  70. 70.
    Samuelson, W., Zeckhauser, R.: Status quo bias in decision making. J. Risk Uncertain. 1, 7–59 (1988)CrossRefGoogle Scholar
  71. 71.
    San Pedro, J., Proserpio, D., Oliver, N.: Mobiscore: towards universal credit scoring from mobile phone data. In: Proceedings of the International Conference on User Modeling, Adaptation and Personalization (UMAP), pp. 195–207 (2015)Google Scholar
  72. 72.
    Short, M.B., D’Orsogna, M.R., Pasour, V.B., Tita, G.E., Brantingham, P.J., Bertozzi, A.L., Chayes, L.B.: A statistical model of criminal behavior. Math. Models Methods Appl. Sci. 18 (supp01), 1249–1267 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  73. 73.
    Singh, V.K., Freeman, L., Lepri, B., Pentland, A.: Predicting spending behavior using socio-mobile features. In: 2013 International Conference on Social Computing (SocialCom), pp. 174–179. IEEE, New York (2013)Google Scholar
  74. 74.
    Singh, V.K., Bozkaya, B., Pentland, A.: Money walks: implicit mobility behavior and financial well-being. PLoS One 10 (8), e0136628 (2015)CrossRefGoogle Scholar
  75. 75.
    Smith-Clarke, C., Mashhadi, A., Capra, L.: Poverty on the cheap: estimating poverty maps using aggregated mobile communication networks. In: Proceedings of the 32nd ACM Conference on Human Factors in Computing Systems (CHI2014) (2014)Google Scholar
  76. 76.
    Soto, V., Frias-Martinez, V., Virseda, J., Frias-Martinez, E.: Prediction of socioeconomic levels using cell phone records. In: Proceedings of the International Conference on UMAP, pp. 377–388 (2011)Google Scholar
  77. 77.
    Staiano, J., Oliver, N., Lepri, B., de Oliveira, R., Caraviello, M., Sebe, N.: Money walks: a human-centric study on the economics of personal mobile data. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, pp. 583–594. ACM, New York (2014)Google Scholar
  78. 78.
    Staiano, J., Zyskind, G., Lepri, B., Oliver, N., Pentland, A.: The rise of decentralized personal data markets. In: Shrier, D., Pentland, A. (eds.) Trust::Data: A New Framework for Identity and Data Sharing. CreateSpace Independent Publishing Platform (2016)Google Scholar
  79. 79.
    Sweeney, L.: Discrimination in online ad delivery. Available at SSRN: (2013)
  80. 80.
    Szabo, N.: Formalizing and securing relationships on public networks. First Monday 2 (9) (1997)Google Scholar
  81. 81.
    Thomas, L.: Consumer Credit Models: Pricing, Profit, and Portfolios. Oxford University Press, New York (2009)CrossRefGoogle Scholar
  82. 82.
    Tizzoni, M., Bajardi, P., Decuyper, A., Kon Kam King, G., Schneider, C.M., Blondel, V., Smoreda, Z., Gonzalez, M.C., Colizza, V.: On the use of human mobility proxies for modeling epidemics. PLoS Comput. Biol. 10 (7) (2014)Google Scholar
  83. 83.
    Tobler, C.: Limits and potential of the concept of indirect discrimination. Technical Report, European Network of Legal Experts in Anti-Discrimination (2008)Google Scholar
  84. 84.
    Toole, J.L., Eagle, N., Plotkin, J.B.: Spatiotemporal correlations in criminal offense records. ACM Trans. Intell. Syst. Technol. 2 (4), 38:1–38:18 (2011)Google Scholar
  85. 85.
    Traunmueller, M., Quattrone, G., Capra, L.: Mining mobile phone data to investigate urban crime theories at scale. In: Proceedings of the International Conference on Social Informatics, pp. 396–411 (2014)Google Scholar
  86. 86.
    Tufekci, Z.: Algorithmic harms beyond Facebook and Google: emergent challenges of computational agency. Colorado Technol. Law J. 13, 203–218 (2015)Google Scholar
  87. 87.
    Tverksy, A., Kahnemann, D.: Judgment under uncertainty: heuristics and biases. Science 185 (4157), 1124–1131 (1974)CrossRefGoogle Scholar
  88. 88.
    Venerandi, A., Quattrone, G., Capra, L., Quercia, D., Saez-Trumper, D.: Measuring urban deprivation from user generated content. In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW2015) (2015)Google Scholar
  89. 89.
    Vescovi, M., Perentis, C., Leonardi, C., Lepri, B., Moiso, C.: My data store: Toward user awareness and control on personal data. In: Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication, pp. 179–182 (2014)Google Scholar
  90. 90.
    Wang, T., Rudin, C., Wagner, D., Sevieri, R.: Learning to detect patterns of crime. In: Machine Learning and Knowledge Discovery in Databases, pp. 515–530. Springer, Berlin (2013)Google Scholar
  91. 91.
    Wang, H., Li, Z., Kifer, D., Graif, C.: Crime rate inference with big data. In: Proceedings of International Conference on KDD (2016)CrossRefGoogle Scholar
  92. 92.
    Want, R., Pering, T., Danneels, G., Kumar, M., Sundar, M., Light, J.: The personal server: changing the way we think about ubiquitous computing. In: Proceedings of 4th International Conference on Ubiquitous Computing, pp. 194–209 (2002)Google Scholar
  93. 93.
    Weisburd, D.: Place-based policing. Ideas Am. Policing 9, 1–16 (2008)Google Scholar
  94. 94.
    Wesolowski, A., Eagle, N., Tatem, A., Smith, D., Noor, R., Buckee, C.: Quantifying the impact of human mobility on malaria. Science 338 (6104), 267–270 (2012)CrossRefGoogle Scholar
  95. 95.
    Wesolowski, A., Stresman, G., Eagle, N., Stevenson, J., Owaga, C., Marube, E., Bousema, T., Drakeley, C., Cox, J., Buckee, C.O.: Quantifying travel behavior for infectious disease research: a comparison of data from surveys and mobile phones. Sci. Rep. 4 (2014)Google Scholar
  96. 96.
    Willson, M.: Algorithms (and the) everyday. Inf. Commun. Soc. 20, 137–150 (2017)CrossRefGoogle Scholar
  97. 97.
    Wilson, R., Erbach-Schoenengerg, E., Albert, M., Power, D., Tudge, S., Gonzalez, M., et al.: Rapid and near real-time assessments of population displacement using mobile phone data following disasters: the 2015 Nepal earthquake. PLoS Current Disasters, February 2016Google Scholar
  98. 98.
    Zang, H., Bolot, J.: Anonymization of location data does not work: a large-scale measurement study. In: Proceedings of 17th ACM Annual International Conference on Mobile Computing and Networking, pp. 145–156 (2011)Google Scholar
  99. 99.
    Zarsky, T.Z.: Automated prediction: Perception, law and policy. Commun. ACM 4, 167–186 (1989)Google Scholar
  100. 100.
    Zarsky, T.: The trouble with algorithmic decisions: an analytic road map to examine efficiency and fairness in automated and opaque decision making. Sci. Technol. Hum. Values 41 (1), 118–132 (2016)CrossRefGoogle Scholar
  101. 101.
    Zyskind, G., Nathan, O., Pentland, A.: Decentralizing privacy: using blockchain to protect personal data. In: Proceedings of IEEE Symposium on Security and Privacy Workshops, pp. 180–184 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Bruno Lepri
    • 1
    Email author
  • Jacopo Staiano
    • 2
  • David Sangokoya
    • 3
  • Emmanuel Letouzé
    • 3
    • 4
  • Nuria Oliver
    • 3
  1. 1.Fondazione Bruno KesslerTrentoItaly
  2. 2.Fortia Financial SolutionsParisFrance
  3. 3.Data-Pop AllianceNew YorkUSA
  4. 4.MIT Media LabCambridgeUSA

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