Big Data as a field has grown exponentially, with large corporations relying on private user data in exchange for services rendered. It is acceptable for data to be collected when there is consent from the user, but it is unethical to collect private and intimate user data and store it illegally. Guidelines and laws that govern computing ethics should be able to guide corporations and users in terms of what is acceptable and ethical in terms of privacy. Currently the guidelines are not adequate in addressing the user privacy needs and the intention of the research is to identify the gap and pave a way to addressing the ethical dilemma of protecting user privacy while ensuring the field of Big Data can also thrive. It must be understood that there are a number of benefits from Big Data and the ability to derive trends from collected information. Big Data can help businesses to be more competitive, help to predict natural disasters, assist insurance companies in evaluating emerging trends and product development, and assist governments in determining needs of citizens. On the other hand, the same data can be used to reject medical, finance and insurance claims to users, spy on individuals and disadvantage them due to the privileged information that is known about them. Ethics and laws need to keep the balance and the profession in check, not only to protect it, but also to preserve its integrity and this research goes a long way to address that.

Computing technology has advanced a great deal and has also exposed people to new ethical challenges, including “privacy, intellectual property, and intellectual freedom” (Al-fedaghi, 2018). Major corporations such as Facebook and Google rely on Big Data and most people, reasonably, understand that their personal information is exposed to these companies, but are not aware that the information might be combined with yet other Big Data to expose more facts about them (Booch, 2014). Such data collection is often collected without consent, such as the recording of sounds by a mobile device while not in use, and such gathering of data is expected to increase (Jurkiewicz, 2018).

Thuraisingham (2015), Cumbley and Church (2013), and Kerr and Earle (2013) agree that the use of algorithms and collation of data may be used to reveal certain information about a person that they would have preferred to remain private and this information may be used by government to disadvantage the person, such as when they demand that privacy and security are bypassed to reveal confidential data (Shamsi & Khojaye, 2018). While Big Data seeks to reveal information, privacy sits on the other end to conceal information (O’Leary, 2015), and therefore ethics must be understood and applied appropriately. Big Data does however offer great advantage and opportunity for the economy (Tene, 2013).

Jones (2016) makes a noteworthy observation that several professional bodies in computing have certain codes of ethics that members must abide by, but the issue is that the standards are only expected from members. Various laws are also instrumental in the curbing of unethical practices in the profession, but the issue is in consistency between countries (Jones, 2016). There are more stringent approaches to information ethics, such as the Freedom of Information Act in the UK, that state organizations act in an honest manner when dealing with information (Mcbride, 2014). Mingers and Walsham (2010) opine that the law should hold certain norms that deconflict ethical dilemmas between communities and stakeholders. Vardi (2019) is not convinced by the approach taken by most institutions of learning when it comes to the teaching of computer ethics, which he calls, “diagnosis and remedy”; but rather advocates for more stringent policies and laws.

The European Union has moved to enact such stringent laws, especially in controlling Big Data, and they require all computer code to be “understandable” to citizens and there are hefty fines for breaking the law and therefore all organizations that collect data have an obligation to be transparent (Jurkiewicz, 2018). Thuraisingham (2015) does however spot a contradiction in certain regulatory requirements that demand information to be kept by organizations for a given period, which increases the risk of privacy violation through Big Data collection. Laws are not enough, but “privacy-preserving” and “data-anonymity” methods are required to maximize privacy for the user (Shamsi & Khojaye, 2018). Guidelines and policies developed must strike a fine balance between protectionism and the enormous rewards brought about by Big Data (Tene, 2013).

Information is being created at a vast speed and in varying forms, and Big Data takes care of how to process, store and even how to disseminate this vast amount of information, but has also brought about new ethical issues, especially as related to privacy. Big Data has the potential to store even the smallest, seemingly, unimportant data from users, and this information can be collated to create and deduce certain facts about the owner of the data and in turn violate the privacy of the individual. Computer ethics is not adequate in its current form to address the issue of privacy within the domain of big data, especially when governments themselves are the generators and users of Big Data, they are reluctant to enact laws that can completely cover the privacy of user data without barring themselves from utilizing this data. On the other hand, organizations that rely on Big Data as a business model must also, to a certain extent, be protected to grow the Big Data market and continue to pioneer and innovate.

Article extract from research into Big Data Ethics by Mr Sandiso Thwala (BSc. Hons Computing – UNISA)

References

Al-fedaghi, S. S. (2018) ‘Thinging Ethics for Software Engineers’, International Journal of Computer Science and Information Security, 16(7). Available at: https://ssrn.com/abstract=3257050.
Baker, M. J. (2001) ‘Selecting a Research Methodology’, The Marketing Review, 1(3), pp. 373–397. doi: 10.1362/1469347002530736.
Booch, G. (2014) ‘The human and ethical aspects of big data’, IEEE Software. IEEE, 31(1), pp. 20–22. doi: 10.1109/MS.2014.16.
Brannen, J. (2005) ‘Mixing methods: The entry of qualitative and quantitative approaches into the research process’, International Journal of Social Research Methodology, 8(3), pp. 173–184. doi: 10.1080/13645570500154642.
Bryman, A., Becker, S. and Sempik, J. (2008) ‘Quality criteria for quantitative, qualitative and mixed methods research: A view from social policy’, International Journal of Social Research Methodology, 11(4), pp. 261–276. doi: 10.1080/13645570701401644.
Cumbley, R. and Church, P. (2013) ‘Is Big Data creepy?’, Computer Law and Security Review. Elsevier Ltd, 29(5), pp. 601–609. doi: 10.1016/j.clsr.2013.07.007. Ess, C. (2009) ‘Floridi’s philosophy of information and information ethics: Current perspectives, future directions’, Information Society, 25(3), pp. 159–168. doi: 10.1080/01972240902848708.
Gaff, B. M., Sussman, H. E. and Geetter, J. (2014) ‘Privacy and Big Data’, Computer. IEEE, 47(6), pp. 7–9. Available at: https://0-doi-org.oasis.unisa.ac.za/10.1109/MC.2014.161.
Hall, B. R. (2014) ‘A synthesized definition of computer ethics’, ACM SIGCAS Computers and Society, 44(3), pp. 21–35. doi: 10.1145/2684097.2684102.
Johnson, R. B., Onwuegbuzie, A. J. and Turner, L. A. (2007) ‘Toward a Definition of Mixed Methods Research’, Journal of MixedMethods Research, 1(2), pp. 112–133. doi: 10.1177/1558689806298224.
Jones, S. (2016) ‘Doing the right thing: computer ethics pedagogy revisited’, Journal of Information, Communication and Ethics in Society, 14(1), pp. 33–48. doi: 10.1108/JICES- 07-2014-0033.
Jurkiewicz, C. L. (2018) ‘Big Data, Big Concerns: Ethics in the Digital Age’, Public Integrity, 20(1), pp. 46–59. doi: 10.1080/10999922.2018.1448218.
Kerr, I. and Earle, J. (2013) ‘Prediction, Preemption, Presumption: How Big Data Threatens Big Picture Privacy’, Stanford Law Review Online, 66, pp. 65–72. Available at: https://0-heinonline- org.oasis.unisa.ac.za/HOL/Page?public=true&handle=hein.journals/slro66&div=10&start_p age=65&collection=journals&set_as_cursor=0&men_tab=srchresults.
Khalil, O. E. m. and Seleim, A. A. s. (2012) ‘Attitudes towards information ethics: A view from Egypt’, Journal of Information, Communication and Ethics in Society, 10(4), pp. 240–doi: 10.1108/14779961211285872.
Mcbride, N. K. (2014) ‘ACTIVE ethics: An information systems ethics for the internet age’, Journal of Information, Communication and Ethics in Society, 12(1), pp. 21–43. doi: 10.1108/JICES-06-2013-0017.
Mingers, J. and Walsham, G. (2010) ‘Toward ethical information systems: The contribution of discourse ethics’, MIS Quarterly, 34(4), pp. 833–854. doi: 10.2307/25750707.
O’Leary, D. E. (2015) ‘Big data and privacy: Emerging issues’, IEEE Intelligent Systems. IEEE, 30(6), pp. 92–96. doi: 10.1109/MIS.2015.110.
Onyancha, O. B. (2015) ‘An informetrics view of the relationship between internet ethics, computer ethics and cyberethics’, Library Hi Tech, 33(3), pp. 387–408. doi: 10.1108/LHT- 04-2015-0033.
Sandelowski, M. and Barroso, J. (2003) ‘Writing the Proposal for a Qualitative Research Methodology Project’, Qualitative Health Research, 13(6), pp. 781–820. doi: 10.1177/1049732303255474.
Shamsi, J. A. and Khojaye, M. A. (2018) ‘Understanding privacy violations in big data systems’, IT Professional. IEEE, 20(3), pp. 73–81. doi: 10.1109/MITP.2018.032501750.
Sofaer, S. (2002) ‘Qualitative Research Methods’, International Journal for Quality in
Health Care, 14(4), pp. 329–336. Available at: https://doi.org/10.1093/intqhc/14.4.329.
Sogunro, O. A. (2002) ‘Selecting a quantitative or qualitative research methodology: An experience’, Educational Research Quarterly, 26(1), pp. 3–10. Available at: http://0-search.ebscohost.com.oasis.unisa.ac.za/login.aspx?direct=true&db=eue&AN=507783791
&site=eds-live&scope=site.
Stander, F., Rothmann, S. and Botha, E. (2014) ‘Ensuring the Quality of the Findings of Qualitative Research: Looking at Trustworthiness Criteria’, Journal of Emerging Trends in Educational Research and Policy Studies, 5(2), pp. 272–281. Available at: http://hdl.handle.net/123456789/256.
Taherdoost, H. (2016) ‘Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research’, International Journal of Academic Research in Management, 5(2), pp. 18–27. doi: 10.2139/ssrn.3205035.
Tene, J. P. & O. (2013) ‘Privacy and Big Data: Making Ends Meet’, Stanford Law Review Online, 66, pp. 25–34. doi: 10.5121/ijgca.2012.3203.
Thuraisingham, B. (2015) ‘Big Data Security and Privacy’, in Proceedings of the 5th ACM Conference on Data and Application Security and Privacy. ACM, pp. 279–280. Available at: https://0-doi-org.oasis.unisa.ac.za/10.1145/2699026.2699136.
Vardi, M. Y. (2019) ‘Are we having an ethical crisis in computing?’, Communications of the ACM, 62(1), p. 7. doi: 10.1145/3292822.
Volkman, R. (2010) ‘Why Information Ethics must begin with Virtue Ethics’, Metaphilosophy, 41(3), pp. 380–401. doi: 10.1002/9781444396836.ch9.
Williams, C. (2007) ‘Research Methods’, Journal of Business & Economic Research, 5(3), pp. 65–72. Available at: https://doi.org/10.19030/jber.v5i3.2532.
Zdravkova, K. (2014) ‘Learning computer ethics and social responsibility with tabletop role- playing games’, Journal of Information, Communication and Ethics in Society, 12(1), pp. 60–75. doi: 10.1108/JICES-09-2013-0038.