Current Psychiatry Reports

, 18:112 | Cite as

Automated Decision-Making and Big Data: Concerns for People With Mental Illness

  • Scott Monteith
  • Tasha Glenn
Psychiatry in the Digital Age (JS Luo, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Psychiatry in the Digital Age


Automated decision-making by computer algorithms based on data from our behaviors is fundamental to the digital economy. Automated decisions impact everyone, occurring routinely in education, employment, health care, credit, and government services. Technologies that generate tracking data, including smartphones, credit cards, websites, social media, and sensors, offer unprecedented benefits. However, people are vulnerable to errors and biases in the underlying data and algorithms, especially those with mental illness. Algorithms based on big data from seemingly unrelated sources may create obstacles to community integration. Voluntary online self-disclosure and constant tracking blur traditional concepts of public versus private data, medical versus non-medical data, and human versus automated decision-making. In contrast to sharing sensitive information with a physician in a confidential relationship, there may be numerous readers of information revealed online; data may be sold repeatedly; used in proprietary algorithms; and are effectively permanent. Technological changes challenge traditional norms affecting privacy and decision-making, and continued discussions on new approaches to provide privacy protections are needed.


Algorithms Big data Mental illness Automated decision-making Privacy 


Compliance with Ethical Standards

Conflict of Interest

Scott Monteith declares that he has no conflict of interest.

Tasha Glenn shares a patent for ChronoRecord software distributed by the ChronoRecord Association, which is a 501(c)(3) non-profit organization. Dr. Glenn does not receive financial compensation from the association.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    •Pasquale F. The black box society. The secret algorithms that control money and information. Cambridge: Harvard University Press; 2015. Book for general public on widespread use and implications of algorithmic decision-making.Google Scholar
  2. 2.
    Markus ML, Topi H. Big data, big decisions for science, society, and business: report on a research agenda setting workshop. ACM Technical Report. 2015. Accessed 1 Aug 2016.
  3. 3.
    Glenn T, Monteith S. New measures of mental state and behavior based on data collected from sensors, smartphones, and the Internet. Curr Psychiatr Rep. 2014;16:1.Google Scholar
  4. 4.
    IDC. The digital universe of opportunities: rich data and the increasing value of the Internet of Things. 2014. Accessed 1 Aug 2016.
  5. 5.
    MIT Technology Review. Big data gets personal business report. 2013. Accessed 1 Aug 2016.
  6. 6.
    Cormen TH, Leiserson CE, Rivest RL, et al. Introduction to algorithms. 3rd ed. Cambridge: MIT Press; 2009.Google Scholar
  7. 7.
    Pariser E. The filter bubble: how the new personalized web is changing what we read and how we think. Penguin Books; 2011.Google Scholar
  8. 8.
    Citron DK. Technological due process. Wash Univ Law Rev. 2008;85:1249–313.Google Scholar
  9. 9.
    Christin A. From daguerreotypes to algorithms: machines, expertise, and three forms of objectivity. ACM SIGCAS Comput Soc. 2016;46:27–32.CrossRefGoogle Scholar
  10. 10.
    McAfee A, Brynjolfsson E, Davenport TH, et al. Big data. The management revolution. Harv Bus Rev. 2012;90:61–7.Google Scholar
  11. 11.
    •Executive Office of the President. Big data: a report on algorithmic systems, opportunity, and civil rights. 2016. Accessed 1 Aug 2016. Report of potential negative impacts of big data on civil rights with recommendations.
  12. 12.
    Guszcza J, Schweidel D, Dutta S. The personalized and the personal: socially responsible innovation through big data. Deloitte Review No 14. 2014. Accessed 1 Aug 2016.
  13. 13.
    WEF (World Economic Forum). Personal data: the emergence of a new asset class. 2011. Accessed 1 Aug 2016.
  14. 14.
    FTC (Federal Trade Commission). Big data: a tool for inclusion or Exclusion? Understanding the issues (FTC Report). 2016. Accessed 1 Aug 2016.
  15. 15.
    Boyd D, Crawford K. Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf Commun Soc. 2012;15:662–79.CrossRefGoogle Scholar
  16. 16.
    •Tene O, Polonetsky J. A theory of creepy: technology, privacy and shifting social norms. Yale JL Technol. 2013;16:59. Report on big data and changing social norms from legal perspective.Google Scholar
  17. 17.
    NIMH. Statistics. 2016. Accessed 1 Aug 2016.
  18. 18.
    Glenn T, Monteith S. Privacy in the digital world: medical and health data outside of HIPAA protections. Curr Psychiatr Rep. 2014;16:494.CrossRefGoogle Scholar
  19. 19.
    Monteith S, Glenn T, Bauer R, et al. Availability of prescription drugs for bipolar disorder at online pharmacies. J Affect Disord. 2016;193:59–65.CrossRefPubMedGoogle Scholar
  20. 20.
    OPC (Office of the Privacy Commissioner of Canada). Metadata and privacy—a technical and legal overview. 2014. Accessed 1 Aug 2016.
  21. 21.
    •ACLU of California. Metadata: piecing together a Format solution. 2014. Accessed 1 Aug 2016. Report on the importance and sensitivity of metadata.
  22. 22.
    Christovich MM. Why should we care what Fitbit shares—a proposed statutory solution to protect sensitive personal fitness information. Hast Commun Ent LJ. 2016;38:91.Google Scholar
  23. 23.
    Schneier B. The Internet of Things that talk about you behind your back. Schneier on Security. 2016. Accessed 1 Aug 2016.
  24. 24.
    •Pentland A. Reinventing society in the wake of big data. Edge. 2012. Accessed 1 Aug 2016. Short interview with Alex (Sandy) Pentland, pioneering computer scientist from MIT Media Lab.
  25. 25.
    Martin KE. Ethical issues in the big data industry. MIS Quarterly Executive. 2015 (14:2).Google Scholar
  26. 26.
    GAO (Government Accountability Office). Information resellers: consumer privacy framework needs to reflect changes in technology and the marketplace. 2013. Accessed 1 Aug 2016.
  27. 27.
    Washington AL. Can big data be described as a data supply chain? 2014. Available at SSRN: Accessed 1 Aug 2016.
  28. 28.
    ••PCAST (President’s Council of Advisors on Science and Technology). Big data and privacy: a technological perspective. 2014. Accessed 1 Aug 2016. Report on challenges to privacy from big data with recommendations.
  29. 29.
    Jagadish HV, Gehrke J, Labrinidis A, et al. Big data and its technical challenges. Commun ACM. 2014;57:86–94.CrossRefGoogle Scholar
  30. 30.
    Crawford, K. The hidden biases in big data. Harv Bus Rev. 2013. Accessed 1 Aug 2016.
  31. 31.
    Monteith S, Glenn T, Geddes J, et al. Big data are coming to psychiatry: a general introduction. Int J Bipolar Disord. 2015;3(1):21.CrossRefPubMedPubMedCentralGoogle Scholar
  32. 32.
    Monteith S, Glenn T, Geddes J, et al. Big data for bipolar disorder. Int J Bipolar Disord. 2016;4(1):10.CrossRefPubMedPubMedCentralGoogle Scholar
  33. 33.
    ••NRC (National Research Council US) Commititee on the Analysis of Massive Data. Frontiers in massive data analysis. 2013. Accessed 1 Aug 2016. Overview book on the technical challenges in the analysis of big data.
  34. 34.
    Bollier D, Firestone CM, The promise and peril of big data. Aspen Institute, Communications and Society Program: Washington; 2010.Google Scholar
  35. 35.
    Abdou HA, Pointon J. Credit scoring, statistical techniques and evaluation criteria: a review of the literature. Intell Syst Account Finan Manag. 2011;18:59–88.CrossRefGoogle Scholar
  36. 36.
    Diakopoulos N. Accountability in algorithmic decision making. Commun ACM. 2016;59:56–62.CrossRefGoogle Scholar
  37. 37.
    Kraemer F, van Overveld K, Peterson M. Is there an ethics of algorithms? Ethics Inf Technol. 2011;13:251–60.CrossRefGoogle Scholar
  38. 38.
    Fan J, Han F, Liu H. Challenges of big data analysis. Natl Sci Rev. 2014;1:293–314.CrossRefPubMedPubMedCentralGoogle Scholar
  39. 39.
    Domingos P. A few useful things to know about machine learning. Commun ACM. 2012;55:78–87.CrossRefGoogle Scholar
  40. 40.
    Pham HN, Triantaphyllou E. The impact of overfitting and overgeneralization on the classification accuracy in data mining. In: Maimon O, Rokach L, editors. Soft computing for knowledge discovery and data mining. Springer US. 2008. p. 391–431.Google Scholar
  41. 41.
    Hand DJ, Adams NM. Selection bias in credit scorecard evaluation. J Oper Res Soc. 2014;65:408–15.CrossRefGoogle Scholar
  42. 42.
    Varian HR. Beyond big data. Bus Econ. 2014;49:27–31.CrossRefGoogle Scholar
  43. 43.
    Andrejevic M. Big data, big questions: the big data divide. Int J Commun. 2014;8:1673–89.Google Scholar
  44. 44.
    Benkler Y. Degrees of freedom, dimensions of power. Daedalus. 2016;145:18–32.CrossRefGoogle Scholar
  45. 45.
  46. 46.
    MacCarthy M. New directions in privacy: disclosure, unfairness and externalities. ISJLP. 2010;6:425.Google Scholar
  47. 47.
    Fairfield JA, Engel C. Privacy as a public good. Duke Law J. 2015;65:385–569.Google Scholar
  48. 48.
  49. 49.
    Schmitz A. Secret consumer scores and segmentations: separating consumer ‘haves’ from ‘have-nots’. Michigan State Law Rev. 2014:1411.Google Scholar
  50. 50.
    Pasquale FA. Restoring transparency to automated authority. Seton Hall Research Paper. 2011;(2010-28).Google Scholar
  51. 51.
    Gillepsie T. The relevance of algorithms. In: Gillespie T, Boczkowski PJ, Foot KA, editors. Media technologies: essays on communication, materiality, and society. Cambridge: MIT Press; 2014. p. 167–95.Google Scholar
  52. 52.
    Kerr I, Earle J. Prediction, preemption, presumption: how big data threatens big picture privacy. Stanf Law Rev Online. 2013;66:65.Google Scholar
  53. 53.
    Dixon P, Gellman B. The scoring of America: how secret consumer scores threaten your privacy and your future. World Privacy Forum. 2014. Accessed 1 Aug 2016.
  54. 54.
    FDIC (Federal Deposit Insurance Corporation). Scoring and modeling. 2007. Accessed 1 Aug 2016.
  55. 55.
    Citron DK, Pasquale FA. The scored society: due process for automated predictions. Washington Law Rev. 2014;89.Google Scholar
  56. 56.
    Kitchin R. Thinking critically about and researching algorithms. The Programmable City. 2014. Accessed 1 Aug 2016.
  57. 57.
    Citron, DK, Open code governance. University of Chicago Legal Forum. 2008. p 355–387. U of Maryland Legal Studies Research Paper No. 2008-1. Accessed 1 Aug 2016.
  58. 58.
    Diakopoulos N. Algorithmic accountability reporting: on the investigation of black boxes. Tow Center for Digital Journalism, Columbia Journalism School, New York NY. 2013. Accessed 1 Aug 2016.
  59. 59.
    Sandvig C, Hamilton K, Karahalios K, et al. Auditing algorithms: research methods for detecting discrimination on Internet platforms. Data and discrimination: converting critical concerns into productive inquiry. 2014.Google Scholar
  60. 60.
    Romei A, Ruggieri S. A multidisciplinary survey on discrimination analysis. Knowl Eng Rev. 2014;29:582–638.CrossRefGoogle Scholar
  61. 61.
    Hoofnagle CJ. How the fair credit reporting act regulates big data (September 10, 2013). Future of privacy forum workshop on big data and privacy: making ends meet. 2013 Accessed 1 Aug 2016.
  62. 62.
    Hicken M. Find out what big data knows about you (it may be very wrong). CNN. 2013. Accessed 1 Aug 2016.
  63. 63.
    Rao A, Schaub F, Sadeh N. What do they know about me? Contents and concerns of online behavioral profiles. arXiv preprint arXiv:1506.01675. 2015.Google Scholar
  64. 64.
    Senate. A review of the data broker industry: collection, use, and sale of consumer data for marketing purposes. 2013. Accessed 1 Aug 2016.
  65. 65.
    Irwin N. Why Ben Bernanke can’t refinance his mortgage. The New York Times. 2014. Accessed 1 Aug 2016.
  66. 66.
    FDA. FDA: software failures responsible for 24 percent of all medical device recalls. FDAnews Device Daily Bulletin. 2012. Accessed 1 Aug 2016.
  67. 67.
    Saks ER. Successful and schizophrenic. The New York Times. 2013. Accessed 1 Aug 2016.
  68. 68.
    Zarsky T. Understanding discrimination in the scored society. Washington Law Review. 2014;89(4).Google Scholar
  69. 69.
    Sareen J, Afifi TO, McMillan KA, et al. Relationship between household income and mental disorders: findings from a population-based longitudinal study. Arch Gen Psychiatry. 2011;68:419–27.CrossRefPubMedGoogle Scholar
  70. 70.
    Mojtabai R, Stuart EA, Hwang I, et al. Long-term effects of mental disorders on employment in the National Comorbidity Survey ten-year follow-up. Soc Psychiatry Psychiatr Epidemiol. 2015;50:1657–68.CrossRefPubMedPubMedCentralGoogle Scholar
  71. 71.
    Cook JA. Employment barriers for persons with psychiatric disabilities: update of a report for the President’s Commission. Psychiatr Serv. 2006;57:1391–405.CrossRefPubMedGoogle Scholar
  72. 72.
    Klee A, Stacy M, Rosenheck R et al. Interest in technology-based therapies hampered by access: a survey of veterans with serious mental illnesses. Psychiatr Rehabil J. 2016.Google Scholar
  73. 73.
    Miller CJ, McInnes DK, Stolzmann K et al. Interest in use of technology for healthcare among veterans receiving treatment for mental health. Telemed J E Health. 2016.Google Scholar
  74. 74.
    Blumberg SJ, Luke JV. Wireless substitution: early release of estimates from the National Health Interview Survey, July–December 2014. CDC, National Center for Health Statistics, Released 06/2015. Accessed 1 Aug 2016.
  75. 75.
    Gonzales AL, Ems L, Suri VR. Cell phone disconnection disrupts access to healthcare and health resources: a technology maintenance perspective. New Media Soc. 2014;1461444814558670.Google Scholar
  76. 76.
    ••Gonzales A. The contemporary US digital divide: from initial access to technology maintenance. Inf Commun Soc. 2016;19:234–48. Study finding that US poor have inconsistent access to technology and resulting impacts.CrossRefGoogle Scholar
  77. 77.
    Lerman J. Big data and its exclusions. Stanf Law Rev Online. 2013;3:66.Google Scholar
  78. 78.
    Nielsen J. The 90-9-1 rule for participation inequality in social media and online communities. 2006. Accessed 1 Aug 2016.
  79. 79.
    Arthur C. What is the 1% rule? The Guardian. 2006. Accessed 1 Aug 2016.
  80. 80.
    Carron-Arthur B, Cunningham JA, Griffiths KM. Describing the distribution of engagement in an Internet support group by post frequency: a comparison of the 90-9-1 principle and Zipf’s Law. Internet Interv. 2014;1:165–8.CrossRefGoogle Scholar
  81. 81.
    •van Mierlo T. The 1% rule in four digital health social networks: an observational study. J Med Internet Res. 2014;16:e33. Study confirming the 90-9-1 rule for participation in 4 mental health related online communities (90% observe only, 9% contribute rarely, and 1% create the vast majority of content).CrossRefPubMedPubMedCentralGoogle Scholar
  82. 82.
    Chew SW, Khoo CS. Comparison of drug information on consumer drug review sites versus authoritative health information websites. J Assoc Inf Sci Technol. 2016;67:333–49.CrossRefGoogle Scholar
  83. 83.
    Hughes S, Cohen D. Can online consumers contribute to drug knowledge? A mixed-methods comparison of consumer-generated and professionally controlled psychotropic medication information on the internet. J Med Internet Res. 2011;13:e53.CrossRefPubMedPubMedCentralGoogle Scholar
  84. 84.
    Weigmann K. Health research 2.0. EMBO Rep. 2014;15:223–6.CrossRefPubMedPubMedCentralGoogle Scholar
  85. 85.
    Schneier B. Phishing has gotten very good. 2013. Accessed 1 Aug 2016.
  86. 86.
    •Jakobsson M, Leddy W. Could you fall for a scam? Spam filters are passe. What we need is software that unmasks fraudsters. IEEE Spectr. 2016;53:40–55. Short article on modern e-mail scams.CrossRefGoogle Scholar
  87. 87.
    Jagatic TN, Johnson NA, Jakobsson M, et al. Social phishing. Commun ACM. 2007;50:94–100.CrossRefGoogle Scholar
  88. 88.
    Mayhorn CB, Murphy-Hill E, Zielinska OA, et al. The social engineering behind phishing. The Next Wave. 2015. Accessed 1 Aug 2016.
  89. 89.
    Vishwanath A, Herath T, Chen R, et al. Why do people get phished? Testing individual differences in phishing vulnerability within an integrated, information processing model. Decis Support Syst. 2011;51:576–86.CrossRefGoogle Scholar
  90. 90.
    Mayhorn CB, Nyeste PG. Training users to counteract phishing. Work. 2012;41(Supplement 1):3549–52.PubMedGoogle Scholar
  91. 91.
    Halevi T, Lewis J, Memon N. A pilot study of cyber security and privacy related behavior and personality traits. Proceedings of the 22nd International Conference on World Wide Web. ACM. 2013.Google Scholar
  92. 92.
    Van Wilsem J. ‘Bought it, but never got it’assessing risk factors for online consumer fraud victimization. Eur Sociol Rev. 2011;jcr053.Google Scholar
  93. 93.
    Buchanan T, Whitty MT. The online dating romance scam: causes and consequences of victimhood. Psychol Crime Law. 2014;20:261–83.CrossRefGoogle Scholar
  94. 94.
    Algarni A, Xu Y, Chan T, et al. Social engineering in social networking sites: how good becomes evil (2014). PACIS (Pacific Asia Conference on Information Systems) 2014 Proceedings. Paper 271. Accessed 1 Aug 2016.
  95. 95.
    Workman M. Wisecrackers: a theory‐grounded investigation of phishing and pretext social engineering threats to information security. J Am Soc Inf Sci Technol. 2008;59:662–74.CrossRefGoogle Scholar
  96. 96.
    University of Exeter School of Psychology. The psychology of scams: provoking and committing errors of judgement. Prepared for the Office of Fair Trading. 2009. Accessed 1 Aug 2016.
  97. 97.
    Downs JS, Holbrook M, Cranor LF. Behavioral response to phishing risk. In: Proceedings of the Anti-Phishing Working Groups 2nd Annual eCrime Researchers Summit 2007 Oct 4 (pp. 37–44). ACM.Google Scholar
  98. 98.
    Sheng S, Holbrook M, Kumaraguru P, et al. Who falls for phish?: a demographic analysis of phishing susceptibility and effectiveness of interventions. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems 2010 Apr 10 (pp. 373–382). ACM.Google Scholar
  99. 99.
    Boodaei M. Mobile users 3 times more vulnerable to phishing attacks. January 4, 2011. Accessed 1 Aug 2016.
  100. 100.
    Gangadharan SP. The downside of digital inclusion: expectations and experiences of privacy and surveillance among marginal Internet users. New Media Soc. 2015:1461444815614053.Google Scholar
  101. 101.
    Claycomb M, Black AC, Wilber C, et al. Financial victimization of adults with severe mental illness. Psychiatr Serv. 2013;64:918–20.CrossRefPubMedPubMedCentralGoogle Scholar
  102. 102.
    James BD, Boyle PA, Bennett DA. Correlates of susceptibility to scams in older adults without dementia. J Elder Abuse Negl. 2014;26:107–22.CrossRefPubMedPubMedCentralGoogle Scholar
  103. 103.
    Lichtenberg PA, Sugarman MA, Paulson D, et al. Psychological and functional vulnerability predicts fraud cases in older adults: results of a longitudinal study. Clin Gerontol. 2016;39:48–63.CrossRefPubMedGoogle Scholar
  104. 104.
    Knauer IB, Zarychta MC. Still no free lunch: recent regulatory initiatives to protect seniors from fraud in the sale of investment products. Secur Regul Law J. 2013;41:397–410.Google Scholar
  105. 105.
    SEC Investor Bulletin: social media and investing-tips for seniors. 2012. Accessed 1 Aug 2016.
  106. 106.
  107. 107.
    Bietz MJ, Hayes GR, Morris ME, et al. Creating meaning in a world of quantified selves. IEEE Pervasive Comput. 2016;15:82–8.CrossRefGoogle Scholar
  108. 108.
    Dredge S. Why the workplace of 2016 could echo Orwell’s 1984. The Guardian. 2015. Accessed 1 Aug 2016.
  109. 109.
    Deloitte. Employers still bullish on wellness programs. Findings from the 2015 surveys of employers and health care consumers. 2015. Accessed 1 Aug 2016.
  110. 110.
    Wolf G. The data-driven life. The New York Times. 2010. Accessed 1 Aug 2016.
  111. 111.
    Hirsch DD. That’s unfair! or is it? Big data, discrimination and the FTC’s unfairness authority. Ky LJ. 2014;103:345.Google Scholar
  112. 112.
    Kroll A. Predictive modeling for life underwriting. Society of Actuaries. Predictive modeling for life insurance seminar May 19, 2010.Google Scholar
  113. 113.
    Hipes C, Lucas J, Phelan JC, et al. The stigma of mental illness in the labor market. Soc Sci Res. 2016;56:16–25.CrossRefPubMedGoogle Scholar
  114. 114.
    Parcesepe AM, Cabassa LJ. Public stigma of mental illness in the United States: a systematic literature review. Adm Policy Ment Health. 2013;40:384–99.CrossRefPubMedGoogle Scholar
  115. 115.
    •Horvitz E, Mulligan D. Data, privacy, and the greater good. Science. 2015;349:253–5. Essay on challenges of balancing privacy and medical research.CrossRefPubMedGoogle Scholar
  116. 116.
    Nisen M. Moneyball at work: they’ve discovered what really makes a great employee. Business Insider. 2013. Accessed 1 Aug 2016.
  117. 117.
    Rosenblat A, Kneese T. Networked employment discrimination. Open Society Foundations’ Future of Work Commissioned Research Papers. 2014. Accessed 1 Aug 2016.
  118. 118.
    Weber L. Today’s personality tests raise the bar for job seekers. Wall Street Journal. 2015. Accessed 1 Aug 2016
  119. 119.
    Goldberg R. lack of trust in Internet privacy and security may deter economic and other online activities. NTIA (National Telecommunications & Information Administration). Accessed 1 Aug 2016.
  120. 120.
    Solove DJ. The future of reputation: gossip, rumor, and privacy on the Internet. Yale University Press; 2007. Accessed 1 Aug 2016.
  121. 121.
    Marthews A, Tucker CE. Government surveillance and Internet search behavior. 2015. Accessed 1 Aug 2016.
  122. 122.
    Bauer R, Conell J, Glenn T, et al. Internet use by patients with bipolar disorder: results from an international multisite survey. Psychiatry Res. 2016;242:388–94.CrossRefPubMedGoogle Scholar
  123. 123.
    Anderson JQ, Rainie L. Millennials will make online sharing in networks a lifelong habit. Pew Research. 2010. Accessed 1 Aug 2016.
  124. 124.
    Bevan JL, Cummings MB, Kubiniec A, et al. How are important life events disclosed on Facebook? Relationships with likelihood of sharing and privacy. Cyberpsychol Behav Soc Netw. 2015;18:8–12.CrossRefPubMedGoogle Scholar
  125. 125.
    •Acquisti A, Brandimarte L, Loewenstein G. Privacy and human behavior in the age of information. Science. 2015;347:509–14. Review of how privacy influences human behavior.CrossRefPubMedGoogle Scholar
  126. 126.
    Johnson B. Privacy no longer a social norm, says Facebook founder. The Guardian. 2010. Accessed 1 Aug 2016.
  127. 127.
    Newman J. Google’s Schmidt roasted for privacy comments. PC World. 2009. Accessed 1 Aug 2016.
  128. 128.
    IOM. Best care at lower cost. The path to continuously learning health care in America. 2013. Accessed 1 Aug 2016.
  129. 129.
    Groves P, Kayyali B, Knott D, et al. The ‘big data’ revolution in healthcare: accelerating value and innovation. McKinsey & Company. 2013. Accessed 1 Aug 2016.
  130. 130.
    Lipman R. Online privacy and the invisible market for our data (January 18, 2016). Penn State Law Rev. 2016.Google Scholar
  131. 131.
    Hartzog W, Selinger E. Big data in small hands. Stanf Law Rev Online. 2013;66:81.Google Scholar
  132. 132.
    ••Solove DJ. Why privacy matters even if you have ‘nothing to hide’. Chronicle of Higher Education. 2011;15. Essay on importance of privacy from an expert in privacy law Google Scholar
  133. 133.
    PCLOB (US Privacy and Civil Liberties Oversight Board). November 12: public meeting on “defining privacy”—transcript. 2014. Accessed 1 Aug 2016.
  134. 134.
    Weiss DC. Chief Justice Roberts admits he doesn’t read the computer fine print. ABA Journal. 2010. Accessed 1 Aug 2016.
  135. 135.
    Abril PS. Private ordering: a contractual approach to online interpersonal privacy. Wake For L Rev. 2010;45:689.Google Scholar
  136. 136.
    Ohm P. Branding privacy. Minn L Rev. 2013;97:907–89.Google Scholar
  137. 137.
    Parker-Pope T. Keeping score on how you take your medicine. The New York Times. 2011 Accessed 1 Aug 2016.
  138. 138.
    •Walker J. Data mining to recruit sick people. Wall Street Journal. 2013. Accessed 1 Aug 2016. Article describing recruiting for clinical trials without using medical records.
  139. 139.
    Gebhart F. New technologies close the recruitment gap. Applied Clinical Trials. 2014. Accessed 1 Aug 2016.
  140. 140.
    ••Kosinski M, Stillwell D, Graepel T. Private traits and attributes are predictable from digital records of human behavior. Proc Natl Acad Sci U S A. 2013;110:5802–5. Highly sensitive personal attributes detected using Facebook likes.CrossRefPubMedPubMedCentralGoogle Scholar
  141. 141.
    Youyou W, Kosinski M, Stillwell D. Computer-based personality judgments are more accurate than those made by humans. Proc Natl Acad Sci U S A. 2015;112:1036–40.CrossRefPubMedPubMedCentralGoogle Scholar
  142. 142.
    De Choudhury M, Gamon M, Counts S, Horvitz E. Predicting depression via social media. InICWSM 2013. Accessed 1 Aug 2016.
  143. 143.
    IOM. Capturing social and behavioral domains in electronic health records. 2014. Accessed 1 Aug 2016.
  144. 144.
    Bughin J, Chui M, Manyika J. Ten IT-enabled business trends for the decade ahead. McKinsey Quarterly. 2013.Google Scholar
  145. 145.
    Gillespie T. Can an algorithm be wrong? Twitter trends, the specter of censorship, and our faith in the algorithms around us. Culture Digitally. 2011. Accessed 1 Aug 2016.
  146. 146.
    Skitka LJ, Mosier K, Burdick MD. Accountability and automation bias. Int J Hum Comput Stud. 2000;52:701–17.CrossRefGoogle Scholar
  147. 147.
    Cummings ML. Automation bias in intelligent time critical decision support systems. In: AIAA 1st Intelligent Systems Technical Conference. 2004;2:557–562.Google Scholar
  148. 148.
    Morse E, Easter R, Lee Y, et al. Integrating systems- and human-centered design approaches for constellation via control authority analysis, Space 2006, SPACE Conferences and Exposition, 2006, San Jose, CA. doi: 10.2514/6.2006-7450. Accessed 1 Aug 2016.

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Michigan State University College of Human MedicineTraverse CityUSA
  2. 2.ChronoRecord Association, Inc.FullertonUSA

Personalised recommendations