My personal beliefs are for the inclusion of ethical concerns in data analytics, so this paper will argue against ethics in data analytics. First, I will argue why data collection and sharing at scale is important. Then, the concept of data privacy will be holistically viewed. Lastly, the issue of identity theft will be addressed.
Opposing Viewpoint Argument
Richards and King argue that human society is being shaped by the big data revolution by affecting decisions and actions in all aspects of life: shopping, voting, interactions, etc. (Richards & King, 2014). It is in the collective interest of everyone to live in a data-open world. Using google and a credit card, anyone can currently obtain another person’s address, phone number, social media accounts, and other personal data. Consumers are supposed to “trust” companies with their personal data, but companies all over the world have proven to not be trustworthy. Facebook’s data breach affected personal data of 87 million users in a single incident (Bradley, 2019). Instead of trusting companies, it should be understood that if data is given to corporations, then that data is available to anyone who wants to access that information.
Privacy is not defined the same throughout the world. In a data-open world, privacy becomes non-existent. The U.N. reaffirmed in 2013 via resolution 68/167 that it is a human right to privacy in the digital age (“III.V.7 United Nations…”, 2015). However, most countries do not abide by these policies, none more evident than China. Every facet of Chinese life is integrated with technology – it is estimated that China has spent the equivalent of nearly $200 billion to adding security and surveillance throughout the country – providing the government with access to personal data of any citizen (Zenz, 2018). The basic premise is that if someone has nothing to hide, then privacy should not be something they seek. This would ultimately lower potential crimes, improving society as a whole.
Identity theft is a large concern and had cost consumers $1.48 billion related to fraud complaints (“Facts Statistics…”, 2019). In a data-open world, identifiable information such as social-security, credit card, and government issued ID numbers would be more easily accessible. Since companies are adding layers of security measures before people can use these, including multi-factor authentication, facial recognition, e-mail verification, and more (Multi-Factor Authentication (MFA) Software, 2019). These additional verification methods can essentially make identification numbers useless, and it should be the consumer’s responsibility to manage these security layers properly.
Conclusion
The big data revolution will forever the alter the way humans use data to live their lives. The data-open world allows the best possible data collection methods and sharing across big data solutions allows for optimal decision-making processes. Instead of trusting companies, consumers should have the mindset of knowing the data they share with corporations will be used and shared. This diminishes the need for privacy – a concept new to human civilization as it is. Instead of privacy, additional layers of security protocols will assist against identity theft and social hacking.
Resources
III.V.7 United Nations General Assembly Resolution 68/167 (On the Right to Privacy in The Digital Age). (2015, June 22). Retrieved from https://referenceworks.brillonline.com/entries/international-law-and-world-order/*-COM_033375.
Bradley, R. (2019, March 12). Data Privacy Concerns: An Overview for 2019. Retrieved from https://medium.com/@the_manifest/data-privacy-concerns-an-overview-for-2019-2ccea79aa6f8.
Facts Statistics: Identity theft and cybercrime. (2019). Retrieved from https://www.iii.org/fact-statistic/facts-statistics-identity-theft-and-cybercrime.
Multi-Factor Authentication (MFA) Software. (2019). Retrieved from https://www.g2.com/categories/multi-factor-authentication-mfa.
Richards, N. M., & King, J. H. (2014). Wake Forest Law Review. Wake Forest Law Review. Retrieved from http://www.informatica.uniroma2.it/upload/2017/IA2/RIchards and King BigDataEthics.pdf
Zenz, A. (2018, March 12). China's Domestic Security Spending: An Analysis of Available Data. Retrieved from https://jamestown.org/program/chinas-domestic-security-spending-analysis-available-data/.
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