The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts

Abstract

The capacity to collect and analyse data is growing exponentially. Referred to as ‘Big Data’, this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: (1) informed consent, (2) privacy (including anonymisation and data protection), (3) ownership, (4) epistemology and objectivity, and (5) ‘Big Data Divides’ created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: (6) the dangers of ignoring group-level ethical harms; (7) the importance of epistemology in assessing the ethics of Big Data; (8) the changing nature of fiduciary relationships that become increasingly data saturated; (9) the need to distinguish between ‘academic’ and ‘commercial’ Big Data practices in terms of potential harm to data subjects; (10) future problems with ownership of intellectual property generated from analysis of aggregated datasets; and (11) the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices.

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Fig. 1

Notes

  1. 1.

    For example, the identification of the presence of diabetes can support targeted marketing (Terry 2012, p. 392).

  2. 2.

    For an overview of sample companies providing such services, see Costa 2014.

  3. 3.

    In some contexts, such as the USA under Health Insurance Portability and Accountability Act, administrative data will be afforded less protection than genomic and similar biobank data despite possessing similar capacities for revealing sensitive aspects of a person’s health. This may be due partly to the possibility of removing identifiers from administrative data without ‘ruining’ the data (Currie 2013) as is an apparent limitation with anonymisation of genomic data (Hansson 2009, p. 10).

  4. 4.

    These forms of biomedical data are incredibly varied and complex, consisting of data produced from a wide variety of sources, including “laboratory auto-analyzers, pharmacy systems, and clinical imaging systems…augmented by data from systems supporting health administrative functions such as patient demographics, insurance coverage, financial data, etc.…clinical narrative information, captured electronically as structured data or transcribed ‘free text’…electronic health records” to name but a few (Safran et al. 2006, p. 2).

  5. 5.

    For instance, Facebook has recently announced plans for “support communities” and “preventative care applications” (Reuters 2014), while Google and Apple have recently released platforms for health and fitness data aggregation (Google Fit and Apple HealthKit/ResearchKit).

  6. 6.

    However, the efficacy of such platforms remains questionable (Butler 2013).

  7. 7.

    See for example the UK Biobank Ethics and Governance Council: http://www.egcukbiobank.org.uk/.

  8. 8.

    By some accounts moral obligations exist for medical research. As suggested by accounts of solidarity-based governance of biomedical Big Data (e.g. Prainsack and Buyx 2013), patients may have a moral duty to participate in research due to the value generated through advances in medical knowledge and treatments (Harris 2005; Schaefer et al. 2009). As participation inherently includes risks, researchers may similarly have a moral obligation to minimise risks as far as possible by extracting maximum value from existing datasets through re-purposing and aggregation (Currie 2013; Harris 2005).

  9. 9.

    The shift to solidarity is also said to free up the “significant resources” currently spent on (re-)consenting procedures for primary and secondary uses of data held in biobanks for research, innovation and infrastructural improvements including interoperability between repositories (Prainsack and Buyx 2013, p. 80). This position rests on the assumption that significant resources are currently being spent on re-consent procedures in particular, which are a central concern for consent and Big Data (e.g. Wellcome Trust 2013), and that these resources would instead be spent on valuable research and structural improvements.

  10. 10.

    The relative lack of reporting on harms stemming from abuses of biomedical data has been noted in a recent Nuffield Council report on the ethics of linking biomedical datasets for research (Nuffield Council on Bioethics 2015). The lack has been largely attributed to a lack of robust reporting mechanisms and empirical research on underreporting, with most cases coming from anecdotal accounts and notable media stories. As a result a lack of evidence of harms should not be considered evidence for a lack of harms.

  11. 11.

    The applicability of theories on the ethics of care (e.g. Gilligan 1982; Noddings 2013; Slote 2007) to Big Data likely extend beyond discrimination against marginalised groups. For example, emphasising responsiveness and relationships between data subjects, custodians and analysts may provide avenues for development of new privacy protection mechanisms and group-level ethics which acknowledge the network ethical effects possible through Big Data (see “Group-level ethics”). While a full account of this and related topics concerning ethics of care goes beyond the scope of this paper, existing work on the applicability of the ethics of care to public health (e.g. Kass 2001) may provide a starting point for future enquiries.

  12. 12.

    With these tendencies noted, the capacity of Big Data to provide scientific explanations of particular types of social phenomena or human behaviours should not be rejected (e.g. Schroeder 2014).

  13. 13.

    For further details on the specification of the right to be forgotten by Google in the EU, see: Advisory Council to Google on the Right to be Forgotten, 2015.

  14. 14.

    Regulatory action may be required, as Big Data creates new opportunities for “data aggregators and miners to…run around health care’s domain-specific protections by creating medical profiles of individuals” not subject to existing legislation (Terry 2012, p. 386), as was the case with the Google Health platform which operated outside of HIPAA restrictions in the United States (Mora 2012, p. 373).

  15. 15.

    As an example of the latter, if biobanking research utilising genome sequences were to reveal that obesity is linked primarily to behaviour rather than genes, or an ethnic group were shown to have a higher genetic pre-disposition to cancer (cf. Angrist 2009; Mathaiyan et al. 2013), well-meaning research may inadvertently lead to future discrimination against these groups.

References

  1. Advisory Council to Google on the Right to be Forgotten. (2015). Report of the advisory council to google on the right to be forgotten. Google Docs. https://drive.google.com/file/d/0B1UgZshetMd4cEI3SjlvV0hNbDA/view?pli=1&usp=embed_facebook. Accessed 19 Mar 2015.

  2. Andrejevic, M. (2014). Big data, big questions the big data divide. International Journal of Communication, 8(0), 17. Accessed 7 Oct 2014.

  3. Angrist, M. (2009). Eyes wide open: The personal genome project, citizen science and veracity in informed consent. Personalized Medicine, 6, 691–699.

    Article  Google Scholar 

  4. Apple. (2014). iBeacon for developers: Apple developer. https://developer.apple.com/ibeacon/. Accessed 17 Nov 2014.

  5. Bail, C. A. (2014). The cultural environment: Measuring culture with big data. Theory and Society, 43(3–4), 465–482. doi:10.1007/s11186-014-9216-5.

    Article  Google Scholar 

  6. Barry, C. A., Stevenson, F. A., Britten, N., Barber, N., & Bradley, C. P. (2001). Giving voice to the lifeworld. More humane, more effective medical care? A qualitative study of doctor-patient communication in general practice. Social Science and Medicine, 53, 487–505. doi:10.1016/s0277-9536(00)00351-8.

    Article  Google Scholar 

  7. Beauchamp, T. L., & Childress, J. F. (2009). Principles of biomedical ethics. New York: Oxford University Press.

    Google Scholar 

  8. Berry, D. M. (2011). The computational turn: Thinking about the digital humanities. Culture Machine, 12(0). ftp://121.171.90.140/big.data/%EB%B9%85%EB%8D%B0%EC%9D%B4%ED%84%B02_20131024_sunup/THE%20COMPUTATIONAL%20TURN%20Digital-Humanities.pdf. Accessed 7 Oct 2014.

  9. Bonilla, D. N. (2014). Information Management professionals working for intelligence organizations: Ethics and deontology implications. Security and Human Rights, 24(3–4), 264–279. doi:10.1163/18750230-02404005.

    Article  Google Scholar 

  10. Booch, G. (2014). The human and ethical aspects of big data. IEEE Software, 31(1), 20–22. Accessed 30 Sept 2014.

  11. Bowker, G. C. (2013). Data flakes: An afterword to “Raw Data”is an oxymoron. Raw data” is an oxymoron. Cambridge: MIT Press. http://www.ics.uci.edu/~vid/Readings/bowker_data_flakes.pdf. Accessed 14 Oct 2014.

  12. Bowker, G. C. (2014). Big data, big questions the theory/data thing. International Journal of Communication, 8(0), 5. Accessed 7 Oct 2014.

  13. Boyd, D., & Crawford, K. (2012). Critical Questions for Big Data: Provocations for a cultural, technological, and scholarly phenomenon. Information Communication & Society, 15(5), 662–679. doi:10.1080/1369118X.2012.678878.

    Article  Google Scholar 

  14. Boye, N. (2012). Co-production of Health enabled by next generation personal health systems. Studies in health technology and informatics, 177, 52–58.

    Google Scholar 

  15. Busch, L. (2014). Big data, big questions a dozen ways to get lost in translation: Inherent challenges in large scale data sets. International Journal of Communication, 8(0), 18. Accessed 7 Oct 2014.

  16. Butler, D. (2013). When Google got flu wrong. Nature, 494(7436), 155–156. doi:10.1038/494155a.

    Article  Google Scholar 

  17. Callebaut, W. (2012). Scientific perspectivism: A philosopher of science’s response to the challenge of big data biology. Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences, 43(1), 69–80. doi:10.1016/j.shpsc.2011.10.007.

    Article  Google Scholar 

  18. Cassa, C. A., Wieland, S. C., & Mandl, K. D. (2008). Re-identification of home addresses from spatial locations anonymized by Gaussian skew. International Journal of Health Geographics, 7(1), 45. doi:10.1186/1476-072X-7-45.

    Article  Google Scholar 

  19. Cheng, L., Shi, C., Wang, X., Li, Q., Wan, Q., Yan, Z., et al. (2013). Chinese biobanks: Present and future. Genetics Research, 95(6), 157–164. doi:10.1017/S0016672313000190.

    Article  Google Scholar 

  20. Choudhury, S., Fishman, J. R., McGowan, M. L., & Juengst, E. T. (2014). Big data, open science and the brain: Lessons learned from genomics. Frontiers in Human Neuroscience, 8, 239. doi:10.3389/fnhum.2014.00239.

    Article  Google Scholar 

  21. Clayton, E. W. (2005). Informed consent and biobanks. Journal of Law, Medicine & Ethics, 33(1), 15–21. doi:10.1111/j.1748-720X.2005.tb00206.x.

    Article  Google Scholar 

  22. Coll, S. (2014). Power, knowledge, and the subjects of privacy: Understanding privacy as the ally of surveillance. Information Communication & Society, 17(10), 1250–1263. doi:10.1080/1369118X.2014.918636.

    Article  Google Scholar 

  23. Collingridge, D. (1980). The social control of technology. Palgrave Macmillan.

  24. Costa, F. F. (2014). Big data in biomedicine. Drug Discovery Today, 19(4), 433–440. doi:10.1016/j.drudis.2013.10.012.

    Article  Google Scholar 

  25. Craig, T. (2011). Privacy and big data. Sebastopol; Cambridge: O’Reilly.

  26. Crawford, K. (2013). The hidden biases in big data. Harvard Business Review. http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/. Accessed 10 Oct 2014.

  27. Crawford, K., Gray, M. L., & Miltner, K. (2014). Critiquing big data: Politics, ethics, epistemology special section introduction. International Journal of Communication, 8, 10. Accessed 2 Oct 2014.

  28. Currie, J. (2013). “Big Data” Versus “Big Brother”: On the appropriate use of large-scale data collections in pediatrics. Pediatrics, 131(Supplement), S127–S132. doi:10.1542/peds.2013-0252c.

    Article  Google Scholar 

  29. Davis, K. (2012). Ethics of big data. O’Reilly Media, Inc.

  30. Dereli, T., Coskun, Y., Kolker, E., Guner, O., Agirbasli, M., & Ozdemir, V. (2014). Big data and ethics review for health systems research in LMICs: Understanding risk, uncertainty and ignorance-and catching the black swans? American Journal of Bioethics, 14(2), 48–50. doi:10.1080/15265161.2013.868955.

    Article  Google Scholar 

  31. Devos, Y., Maeseele, P., Reheul, D., Van Speybroeck, L., & De Waele, D. (2008). Ethics in the societal debate on genetically modified organisms: A (Re)Quest for sense and sensibility. Journal of Agricultural and Environmental Ethics, 21(1), 29–61. doi:10.1007/s10806-007-9057-6.

    Article  Google Scholar 

  32. Docherty, A. (2014). Big data: Ethical perspectives. Anaesthesia, 69(4), 390–391. doi:10.1111/anae.12656.

    Article  Google Scholar 

  33. Dove, E. S., Knoppers, B. M., & Zawati, M. H. (2014). Towards an ethics safe harbor for global biomedical research. Journal of Law and the Biosciences, 1(1), 3–51. doi:10.1093/jlb/lst002.

    Article  Google Scholar 

  34. Enjolras, B. (2014). Big Data and social research: New possibilities and ethical challenges. Tidsskrift for Samfunnsforskning, 55(1), 80–89.

    Google Scholar 

  35. EURORDIS. (2013). Statement on the EP Report on the Protection of Personal Data. http://www.publichealth.ox.ac.uk/helex/Statement%20Data%20Prot%20FINAL.pdf. Accessed 22 Oct 2014.

  36. Fairfield, J., & Shtein, H. (2014). Big data, big problems: Emerging issues in the ethics of data science and journalism. Journal of Mass Media Ethics, 29(1), 38–51. doi:10.1080/08900523.2014.863126.

    Article  Google Scholar 

  37. Fan, W., & Bifet, A. (2013). Mining big data: Current status, and forecast to the future. ACM SIGKDD Explorations Newsletter, 14(2), 1–5. Accessed 2 Oct 2014.

  38. Floridi, L. (2008). The method of levels of abstraction. Minds and Machines, 18(3), 303–329. doi:10.1007/s11023-008-9113-7.

    Article  Google Scholar 

  39. Floridi, L. (2012). Big data and their epistemological challenge. Philosophy & Technology, 25(4), 435–437. doi:10.1007/s13347-012-0093-4.

    Article  Google Scholar 

  40. Floridi, L. (2013). The philosophy of information (Reprint ed.). Oxford: OUP Oxford.

    Google Scholar 

  41. Floridi, L. (Ed.). (2014a). The onlife manifesto. New York: Springer. http://www.springer.com/philosophy/epistemology+and+philosophy+of+science/book/978-3-319-04092-9. Accessed 2 Dec 2014.

  42. Floridi, L. (2014b). Open data, data protection, and group privacy. Philosophy & Technology, 27(1), 1–3. doi:10.1007/s13347-014-0157-8.

    Article  Google Scholar 

  43. Gadamer, H. G. (1976). The historicity of understanding. Harmondsworth: Penguin Books Ltd.

    Google Scholar 

  44. Gadamer, H. G. (2004). Truth and method. London: Continuum International Publishing Group.

    Google Scholar 

  45. General Medical Council. (2008). Consent guidance. http://www.gmc-uk.org/guidance/ethical_guidance/consent_guidance_index.asp.

  46. Gilligan, C. (1982). In a different voice. Cambridge: Harvard University Press.

    Google Scholar 

  47. Goodman, E. (2014). Design and ethics in the era of big data. Interactions, 21(3), 22–24. Accessed 1 Oct 2014.

  48. Habermas, J. (1984). The theory of communicative action. Volume 1: Reason and the rationalization of society. Boston: Beacon.

    Google Scholar 

  49. Habermas, J. (1985). The theory of communicative action. Volume 2: Lifeworld and system: A critique of functionalist reason. Boston: Beacon.

    Google Scholar 

  50. Hansson, M. G. (2009). Ethics and biobanks. British Journal of Cancer, 100(1), 8–12. doi:10.1038/sj.bjc.6604795.

    Article  Google Scholar 

  51. Harris, J. (2005). Scientific research is a moral duty. Journal of Medical Ethics, 31(4), 242–248. doi:10.1136/jme.2005.011973.

    Article  Google Scholar 

  52. Hay, M., Miklau, G., Jensen, D., Towsley, D., & Weis, P. (2008). Resisting structural re-identification in anonymized social networks. Proceedings of the VLDB Endowment, 1(1), 102–114. doi:10.14778/1453856.1453873.

    Article  Google Scholar 

  53. Hayden, E. C. (2012). A broken contract. NATURE PUBLISHING GROUP MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND. http://environmentportal.in/files/file/informed%20consent.pdf. Accessed 7 Oct 2014.

  54. Heidegger, M. (1967). Being and time. Oxford: Blackwell.

    Google Scholar 

  55. Helbing, D., & Balietti, S. (2011). From social data mining to forecasting socio-economic crises. European Physical Journal-Special Topics, 195(1), 3–68. doi:10.1140/epjst/e2011-01401-8.

    Article  Google Scholar 

  56. Higuchi, N. (2013). Three challenges in advanced medicine. Japan Medical Association Journal, 56(6), 437–447.

    Google Scholar 

  57. Hoffman, S. (2014). Citizen science: The law and ethics of public access to medical big data (SSRN Scholarly Paper No. ID 2491054). Rochester, NY: Social Science Research Network. http://papers.ssrn.com/abstract=2491054. Accessed 13 Oct 2014.

  58. Hoffman, S., & Podgurski, A. (2013). Big bad data: Law, public health, and biomedical databases. Journal of Law, Medicine and Ethics, 41(Suppl. 1), 56–60. doi:10.1111/jlme.12040.

    Article  Google Scholar 

  59. IBM. (2014). The four V’s of big data. http://www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed 23 Oct 2014.

  60. Ioannidis, J. P. A. (2013). Informed consent, big data, and the oxymoron of research that is not research. American Journal of Bioethics, 13(4), 40–42. doi:10.1080/15265161.2013.768864.

    Article  Google Scholar 

  61. Joly, Y., Dove, E. S., Knoppers, B. M., Bobrow, M., & Chalmers, D. (2012). Data sharing in the post-genomic world: The experience of the international cancer genome consortium (ICGC) data access compliance office (DACO). PLoS Computational Biology, 8(7), e1002549. doi:10.1371/journal.pcbi.1002549.

    Article  Google Scholar 

  62. Kass, N. E. (2001). An ethics framework for public health. American Journal of Public Health, 91(11), 1776–1782. doi:10.2105/AJPH.91.11.1776.

    Article  Google Scholar 

  63. Kaye, J., Curren, L., Anderson, N., Edwards, K., Fullerton, S. M., Kanellopoulou, N., et al. (2012). From patients to partners: Participant-centric initiatives in biomedical research. Nature Reviews Genetics, 13(5), 371–376. doi:10.1038/nrg3218.

    Article  Google Scholar 

  64. Knobel, C. P. (2010). Ontic occlusion and exposure in sociotechnical systems. University of Pittsburgh. Retrieved from http://deepblue.lib.umich.edu/handle/2027.42/78763.

  65. Krotoski, A. K. (2012). Data-driven research: Open data opportunities for growing knowledge, and ethical issues that arise. Insights: The UKSG Journal, 25(1), 28–32. doi:10.1629/2048-7754.25.1.28.

    Google Scholar 

  66. Laney, D. (2001). 3D data management: Controlling data volume, velocity and variety. META Group Research Note, 6.

  67. Larson, E. B. (2013). Building trust in the power of “big data” research to serve the public good. JAMA, 309(23), 2443–2444. doi:10.1001/jama.2013.5914.

    Article  Google Scholar 

  68. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., et al. (2009). Computational social science. Science, 323(5915), 721–723. doi:10.1126/science.1167742.

    Article  Google Scholar 

  69. Lewis, C. M., Obregón-Tito, A., Tito, R. Y., Foster, M. W., & Spicer, P. G. (2012). The Human Microbiome Project: Lessons from human genomics. Trends in Microbiology, 20(1), 1–4. doi:10.1016/j.tim.2011.10.004.

    Article  Google Scholar 

  70. Liyanage, H., de Lusignan, S., Liaw, S.-T., Kuziemsky, C. E., Mold, F., Krause, P., et al. (2014). Big data usage patterns in the health care domain: A use case driven approach applied to the assessment of vaccination benefits and risks. Contribution of the IMIA Primary Healthcare Working Group. Yearbook of medical informatics, 9(1), 27–35. doi:10.15265/IY-2014-0016.

  71. Lomborg, S., & Bechmann, A. (2014). Using APIs for data collection on social media. Information Society, 30(4), 256–265. doi:10.1080/01972243.2014.915276.

    Article  Google Scholar 

  72. Lupton, D. (2014). The commodification of patient opinion: The digital patient experience economy in the age of big data. Sociology of Health & Illness, 36(6), 856–869. doi:10.1111/1467-9566.12109.

    Article  Google Scholar 

  73. Lynch, C. (2008). Big data: How do your data grow? Nature, 455(7209), 28–29. doi:10.1038/455028a.

    Article  Google Scholar 

  74. Lyon, D. (2003). Surveillance as social sorting: Privacy, risk, and digital discrimination. London: Routledge.

    Google Scholar 

  75. MacIntyre, A. (2007). After virtue: A study in moral theory (3rd ed.). London: Gerald Duckworth & Co Ltd.

    Google Scholar 

  76. Mahajan, R. L., Reed, J., Ramakrishnan, N., Mueller, R., Williams, C. B., & Campbell, T. A. (2012). Cultivating emerging and black swan technologies (Vol. 6, pp. 549–557). Presented at the ASME international mechanical engineering congress and exposition, proceedings (IMECE). doi:10.1115/IMECE2012-89339

  77. Majumder, M. A. (2005). Cyberbanks and other virtual research repositories. Journal of Law, Medicine & Ethics, 33(1), 31–39. doi:10.1111/j.1748-720X.2005.tb00208.x.

    Article  Google Scholar 

  78. Markowetz, A., Błaszkiewicz, K., Montag, C., Switala, C., & Schlaepfer, T. E. (2014). Psycho-Informatics: Big Data shaping modern psychometrics. Medical Hypotheses, 82(4), 405–411. doi:10.1016/j.mehy.2013.11.030.

    Article  Google Scholar 

  79. Master, Z., Campo-Engelstein, L., & Caulfield, T. (2014). Scientists’ perspectives on consent in the context of biobanking research. European Journal of Human Genetics. doi:10.1038/ejhg.2014.143.

    Google Scholar 

  80. Mathaiyan, J., Chandrasekaran, A., & Davis, S. (2013). Ethics of genomic research. Perspectives in Clinical Research, 4(1), 100. doi:10.4103/2229-3485.106405.

    Article  Google Scholar 

  81. McGuire, A. L., Achenbaum, L. S., Whitney, S. N., Slashinski, M. J., Versalovic, J., Keitel, W. A., et al. (2012). Perspectives on human microbiome research ethics. Journal of Empirical Research on Human Research Ethics: An International Journal, 7(3), 1–14. doi:10.1525/jer.2012.7.3.1.

    Article  Google Scholar 

  82. McGuire, A. L., Colgrove, J., Whitney, S. N., Diaz, C. M., Bustillos, D., & Versalovic, J. (2008). Ethical, legal, and social considerations in conducting the Human Microbiome Project. Genome Research, 18(12), 1861–1864. doi:10.1101/gr.081653.108.

    Article  Google Scholar 

  83. McNeely, C. L., & Hahm, J. (2014). The Big (Data) Bang: Policy, prospects, and challenges. Review of Policy Research, 31(4), 304–310. doi:10.1111/ropr.12082.

    Article  Google Scholar 

  84. Mello, M. M., Francer, J. K., Wilenzick, M., Teden, P., Bierer, B. E., & Barnes, M. (2013). Preparing for responsible sharing of clinical trial data. New England Journal of Medicine, 369(17), 1651–1658. doi:10.1056/NEJMhle1309073.

    Article  Google Scholar 

  85. Mittelstadt, B. D., Fairweather, N. B., McBride, N., & Shaw, M. (2011). Ethical issues of personal health monitoring: A literature review. In ETHICOMP 2011 conference proceedings (pp. 313–321). Presented at the ETHICOMP 2011, Sheffield, UK.

  86. Mittelstadt, B. D., Fairweather, N. B., McBride, N., & Shaw, M. (2013). Privacy, risk and personal health monitoring. In ETHICOMP 2013 conference proceedings (pp. 340–351). Presented at the ETHICOMP 2013, Kolding, Denmark.

  87. Mittelstadt, B. D., Fairweather, N. B., Shaw, M., & McBride, N. (2014). The ethical implications of personal health monitoring. International Journal of Technoethics, 5(2), 37–60.

    Article  Google Scholar 

  88. Mittelstadt, B. D., Stahl, B. C., & Fairweather, N. B. (2015). How to shape a better future? Epistemic difficulties for ethical assessment and anticipatory governance of emerging technologies. Ethical Theory and Moral Practice, 1–21. doi:10.1007/s10677-015-9582-8.

  89. Moor, J. (1985). What is computer ethics?*. Metaphilosophy, 16(4), 266–275. doi:10.1111/j.1467-9973.1985.tb00173.x.

    Article  Google Scholar 

  90. Moore, P., Xhafa, F., Barolli, L., & Thomas, A. (2013). Monitoring and detection of agitation in dementia towards real-time and big-data solutions. 2013 Eighth international conference on P2p, parallel, grid, cloud and internet computing (3pgcic 2013), pp 128–135. doi:10.1109/3PGCIC.2013.26

  91. Mora, F. (2012). The demise of google health and the future of personal health records. International Journal of Healthcare Technology and Management, 13(5), 363–377. Accessed 11 Nov 2014.

  92. National Science Foundation. (2014). Critical techniques and technologies for advancing big data science & engineer (BIGDATA): Program Solicitation NSF 14-543. http://www.nsf.gov/pubs/2014/nsf14543/nsf14543.pdf. Accessed 17 Oct 2014.

  93. NHS England (2014). NHS England. The care.data programme: better information means better care. http://www.england.nhs.uk/ourwork/tsd/care-data/. Accessed 11 Nov 2014.

  94. Niemeijer, A. R., Frederiks, B. J., Riphagen, I. I., Legemaate, J., Eefsting, J. A., & Hertogh, C. M. (2010). Ethical and practical concerns of surveillance technologies in residential care for people with dementia or intellectual disabilities: An overview of the literature. International Psychogeriatrics, 22, 1129–1142.

    Article  Google Scholar 

  95. Nissenbaum, H. (2004). Privacy as contextual integrity (SSRN Scholarly Paper No. ID 534622). Rochester, NY: Social Science Research Network. http://papers.ssrn.com/abstract=534622. Accessed 12 Mar 2013.

  96. Noddings, N. (2013). Caring: A relational approach to ethics and moral education. Berkeley: University of California Press.

    Google Scholar 

  97. Nuffield Council on Bioethics. (2015). The collection, linking and use of data in biomedical research and health care: Ethical issues (p. 198). Nuffield Council on Bioethics. http://nuffieldbioethics.org/wp-content/uploads/Biological_and_health_data_web.pdf.

  98. Nunan, D., & Di Domenico, M. (2013). Market research and the ethics of big data. International Journal of Market Research, 55(4), 505. doi:10.2501/IJMR-2013-015.

    Article  Google Scholar 

  99. Oboler, A., Welsh, K., & Cruz, L. (2012a). The danger of big data: Social media as computational social science. First Monday, 17(7). https://www.scopus.com/inward/record.url?eid=2-s2.0-84867308941&partnerID=40&md5=0e4cb2f657154c7f82a76c2a657259ab.

  100. Oboler, A., Welsh, K., & Cruz, L. (2012b). The danger of big data: Social media as computational social science. First Monday, 17(7). http://journals.uic.edu/ojs/index.php/fm/article/view/3993. Accessed 1 Oct 2014.

  101. Pariser, E. (2011). The filter bubble: What the Internet is hiding from you. London: Viking.

    Google Scholar 

  102. Patterson, M. E., & Williams, D. R. (2002). Collecting and analyzing qualitative data: Hermeneutic principles, methods and case examples (Vol. 9). Champaign, IL: Sagamore Publishing, Inc. http://www.treesearch.fs.fed.us/pubs/29421. Accessed 7 Nov 2012.

  103. Pellegrino, E. D., & Thomasma, D. C. (1993). The virtues in medical practice. New York: Oxford University Press.

    Google Scholar 

  104. Prainsack, B., & Buyx, A. (2013). A solidarity-based approach to the governance of research biobanks. Medical Law Review, 21(1), 71–91. doi:10.1093/medlaw/fws040.

    Article  Google Scholar 

  105. Puschmann, C., & Burgess, J. (2014). Big data, big questions metaphors of big data. International Journal of Communication, 8(0), 20. Accessed 7 Oct 2014.

  106. Reuters. (2014, October 3). Facebook plots first steps into healthcare. http://www.telegraph.co.uk/technology/facebook/11139606/Facebook-plots-first-steps-into-healthcare.html. Accessed 15 Nov 2014.

  107. Richards, N. M., & King, J. H. (2013). Three paradoxes of big data. Stanford Law Review Online, 66, 41. Accessed 18 Feb 2015.

  108. Rothstein, M. A., & Shoben, A. B. (2013). An unbiased response to the open peer commentaries on “Does Consent Bias Research?”. The American Journal of Bioethics, 13(4), W1–W4. doi:10.1080/15265161.2013.769824.

    Article  Google Scholar 

  109. Safran, C., Bloomrosen, M., Hammond, W. E., Labkoff, S., Markel-Fox, S., Tang, P. C., et al. (2006). Toward a national framework for the secondary use of health data: An American medical informatics association white paper. Journal of the American Medical Informatics Association, 14(1), 1–9. doi:10.1197/jamia.M2273.

    Article  Google Scholar 

  110. Schadt, E. E. (2012). The changing privacy landscape in the era of big data. Molecular Systems Biology, 8. doi:10.1038/msb.2012.47

  111. Schaefer, G. O., Emanuel, E. J., & Wertheimer, A. (2009). The obligation to participate in biomedical research. JAMA, 302(1), 67–72. Accessed 19 Mar 2015.

  112. Schroeder, R. (2014). Big data and the brave new world of social media research. Big Data & Society, 1(2). doi:10.1177/2053951714563194

  113. Schroeder, R., & Cowls, J. (2014). Big data, ethics, and the social implications of knowledge production. http://dataethics.github.io/proceedings/BigDataEthicsandtheSocialImplicationsofKnowledgeProduction.pdf. Accessed 2 Oct 2014.

  114. Schwandt, T. A. (2000). Three epistemological stances for qualitative inquiry: Interpretivism, hermeneutics, and social constructionism. Handbook of qualitative research (pp. 189–214). Thousand Oaks, CA: Sage.

    Google Scholar 

  115. Shilton, K. (2012). Participatory personal data: An emerging research challenge for the information sciences. Journal of the American Society for Information Science and Technology, 63(10), 1905–1915. doi:10.1002/asi.22655.

    Article  Google Scholar 

  116. Slashinski, M. J., McCurdy, S. A., Achenbaum, L. S., Whitney, S. N., & McGuire, A. L. (2012). “Snake-oil,”“quack medicine,” and “industrially cultured organisms:” biovalue and the commercialization of human microbiome research. BMC medical ethics, 13(1), 28. Accessed 13 Oct 2014.

  117. Slote, M. (2007). The ethics of care and empathy (New Ed edition.). London, New York: Routledge.

  118. Steinsbekk, K. S., Ursin, L. Ø., Skolbekken, J.-A., & Solberg, B. (2013). We’re not in it for the money—lay people’s moral intuitions on commercial use of “their” biobank. Medicine, Health Care and Philosophy, 16(2), 151–162. doi:10.1007/s11019-011-9353-9.

    Article  Google Scholar 

  119. Taylor, L., & Floridi, L. (Eds.). (2015). Group privacy: New challenges of data technologies. New York: Springer (forthcoming).

  120. Tene, O., & Polonetsky, J. (2013). Big data for all: Privacy and user control in the age of analytics. http://heinonlinebackup.com/hol-cgi-bin/get_pdf.cgi?handle=hein.journals/nwteintp11&section=20. Accessed 2 Oct 2014.

  121. Terry, N. (2012). Protecting patient privacy in the age of big data. UMKC L. Rev., 81, 385. Accessed 2 Oct 2014.

  122. Terry, N. (2014). Health privacy is difficult but not impossible in a post-hipaa data-driven world. Chest, 146(3), 835–840. doi:10.1378/chest.13-2909.

    Article  Google Scholar 

  123. The NIH HMP Working Group, Peterson, J., Garges, S., Giovanni, M., McInnes, P., Wang, L., et al. (2009). The NIH human microbiome project. Genome Research, 19(12), 2317–2323. doi:10.1101/gr.096651.109.

    Article  Google Scholar 

  124. van den Berg, B., & van der Hof, S.. (2012). What happens to my data? A novel approach to informing users of data processing practices. First Monday, 17(7). doi:10.5210/fm.v17i7.4010

  125. van der Sloot, B. 2014). Privacy in the Post-NSA Era: Time for a fundamental revision? http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2432104. Accessed 17 Feb 2015.

  126. Watson, R. W. G., Kay, E. W., & Smith, D. (2010). Integrating biobanks: Addressing the practical and ethical issues to deliver a valuable tool for cancer research. Nature Reviews Cancer, 10(9), 646–651. doi:10.1038/nrc2913.

    Article  Google Scholar 

  127. Wellcome Trust. (2013). Impact of the draft European data protection regulation and proposed amendments from the rapporteur of the LIBE committee on scientific research. Wellcome Trust. http://www.wellcome.ac.uk/stellent/groups/corporatesite/@policy_communications/documents/web_document/WTP055584.pdf. Accessed 22 Oct 2014.

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Acknowledgments

The research leading to this work has been funded by a <removed for anonymity> major research grant. An initial version of this paper was discussed at a workshop organised at the <removed for anonymity> on <removed for anonymity>. We wish to acknowledge the extremely valuable feedback received during that meeting and from the two anonymous reviewers. This study was funded by the University of Oxford’s John Fell Fund.

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The authors declare that they have no conflict of interest.

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Correspondence to Brent Daniel Mittelstadt.

Appendix

Appendix

See Table 3.

Table 3 Ethical themes

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Mittelstadt, B.D., Floridi, L. The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts. Sci Eng Ethics 22, 303–341 (2016). https://doi.org/10.1007/s11948-015-9652-2

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Keywords

  • Ethics
  • Big data
  • Bioethics
  • Information ethics
  • Medical ethics
  • Ethical foresight