ETHICAL CONCERNS IN CONSUMER PREDICTIVE ANALYTICS
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Institution
Author’s Note
QUESTION 4
What are the Issues?
The issue in this case study is the use of predictive analytics in measuring and predicting consumer behavior. The case also touches on the extent to which business entities can obtain and use consumer information.
Who are the Stakeholders Involved?
The case study involves business entities that obtain and use consumer information, the consumers whose information is obtained and used in businesses’ marketing policies, and the government, which is responsible for regulating consumer information and the protection of consumer rights.
How would I apply various ethical perspectives to this issue?
The first ethical concern in consumer predictive analytics is about the privacy of consumers. Privacy may mean one’s right to decide what information about them exists in public (Westin, 1968). Notably, privacy rights are protected under the Amendments of the U.S Constitution (U.S. Const. amend. I, III, IV, V). It is incumbent upon businesses to ensure they develop sound policies that protect consumers’ privacy when they conduct predictive analytics and other related research on consumer behavior (Leonard, 2014).
Another ethical concern is the use of a consumer’s data to predict the consumer’s pregnancy. To some people, it may look harmless to obtain, track, and use consumer pregnancy information for business purposes. However, for others, it appears that by obtaining such sensitive consumer information, business entities are unethically taking advantage of their capability to gather data at their advantage (Davenport, 2020). It even gets worse when one figures out that the data is used by businesses to obtain a competitive advantage. When such aims drive businesses, they would quickly and aggressively engage in any conduct, whether ethical or not.
Lastly, consumer predictive analytics increases security risks because a pool of consumers’ data may be exposed to criminals. These security risks may be attributed to inaccurate protection mechanisms of acquired data by businesses (Madden et al., 2017).
What Solutions can I suggest?
Businesses can put in place working on privacy policies to protect the privacy of consumer information. Such policies that protect the consumers’ privacy ensures the consumers get the best experience out of their dealings with a business entity (Krishen et al., 2017). It follows; many businesses, if not all, incorporate privacy policies for the protection of consumer privacy rights. All consumers must go through the privacy policies for them to use the goods or services from the businesses. A good example of a working privacy policy is form Amazon. Amazon’s privacy policy lists all the information that Amazon obtains from its customers (Amazon.com Help: Amazon.com Privacy Notice, 2020). It proceeds to expressly state how the information will be used. Amazon also gives an assurance that the customers’ information shall only be used for the purposes stated in the policy. Interestingly, Amazon provides consumers an option to exclude certain information from being obtained by Amazon.
Another solution for tackling ethical concerns in consumer predictive analytics is the use of data ethics boards (Davenport, 2020). These boards handle all data concerns that arise out of the need to ensure consumer information safety in predictive analytics. In some cases, businesses go-ahead to create offices and employ staff to protect consumer information. This practice is already in use by various businesses globally. For instance, Microsoft uses the Artificial Intelligence Ethics and Effects in Engineering and Research (Aether) Committee to look at data issues (Microsoft, 2020).
In conclusion, the protection of consumer information in predictive analytics remains a challenge in the era of massive competition in the 21st century. It follows, businesses must come up with measures to protect the said information.
REFERENCES
Davenport, T., 2020. The Houston Astros And The Ethical Use Of Data And Analytics. [online] Forbes. Available at: https://www.forbes.com/sites/tomdavenport/2020/03/04/the-houston-astros-and-the-ethical-use-of-data-and-analytics/#7b1387f96f8f [Accessed 19 October 2020].
Krishen, Anjala S., Robyn L. Raschke, Angeline G. Close, and Pushkin Kachroo. 2017. A power-responsibility equilibrium framework for fairness: Understanding consumers’ implicit privacy concerns for location-based services. Journal of Business Research 73: 20–29.
Leonard, P. 2014. Customer data analytics: Privacy settings for ‘Big Data’ business. International Data Privacy Law 4 (1): 53–69.
Madden, Mary and Gilman, Michele E. and Levy, Karen and Marwick, Alice E., Privacy, Poverty and Big Data: A Matrix of Vulnerabilities for Poor Americans (March 9, 2017). 95 Washington University Law Review 53 (2017).
Microsoft. 2020. Responsible AI Principles From Microsoft. [online] Available at: https://www.microsoft.com/en-us/ai/responsible-ai?activetab=pivot1%3aprimaryr6 [Accessed 19 October 2020].
U.S. Const. amend. Amendment I, III, IV, V.
Westin, A.F. 1968. Privacy and freedom. Washington and Lee Law Review 25 (1): 166–170.
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