Difference between revisions of "Ethics of Data Mining"

Revision as of 03:56, 10 February 2023

Data mining, at its core, is simply the process of obtaining large data sets and utilizing various analytical tools to investigate and interpret these data sets. In doing this, data mining is used to discover patterns within these large sets in order to develop and test models based on them. As technological advancements continue to develop, skills, such as data mining, have become more and more prevalent. In fact, many ordinary individuals often interact with data mining tools everyday whether it is through their search history or social media. Through data mining, companies can gain access to intimate user data and use it to generate consumer profiles and intelligence. Data miners often sell the personal information of users and customers to other companies. These companies can then use that information to coordinate business efforts such as targeted marketing.^[1]

Types of Data Mining

Predictive Data Mining

As suggested by the name, predictive data mining is data mining done with the purpose of predicting or forecasting trends. When companies buy or gain access to user data, they can sort through this data and use forecasting tools to create theories based on the obtained user information. This, by and large, makes heavy use of a businesses' analytical software though results from this type of data mining can often prove to be inaccurate or misleading. The most typical use of predictive data mining is through trend forecasting. A company may sort through a customer's purchase or search history to create targeted advertisements based on this data. In this way, such kind of data mining is often referred to as proactive.

Descriptive Data Mining

On the other hand, descriptive data mining takes a more reactive approach. Instead, of making future predictions based on customer data, descriptive data mining relies on using concrete analysis to identify correlational relationships based off of already existing data. Therefore, the data and results provided in this type of data mining is concrete and precise. Businesses gain insight from past data and use this to then learn and establish underlying patterns. For example, a company might keep track of user data on their website to see which part of their site receives the most interaction compared to which parts of their site receive the least amount of interaction. From this, a business might be able to identify touch-points on their website connected to certain user behavior. Businesses often use a combination of both of these data mining techniques to best serve their interests^[2].

Process of Data Mining

Establishing a Business Goal

Before any business delves into the process of data mining, they must first establish a clear goal that they want to achieve. This occurs prior to the establishment of any software and forces the business to clarify what outcome they hope to attain. For example, a business may hope to increase site engagement, user clicks, or company revenue.

Defining the Data

After establishing a clear goal that the business wants to realize, it is time to define the data. This means that companies need to research what kind of data they want to collect as well as how they plan to obtain it. In this way, it forces businesses to realistically think about the scope of the data they will be able to collect and confront constraints related to storage, collection, and analysis processes. Business's also brainstorm all viable sources of data during this time.

Gathering the Data

Relationship Between Data Mining and Social Media

Nature of Relationship

Though there are many ways miners can collect data, social media sites are amongst the largest sources of user information and data mining. Through the nature of social media, many platforms such as Twitter, Instagram, Meta(formerly known as Facebook), and TikTok have access to the personal data of users including gender, ethnicity, age, and even location. Additionally, through each platform's own algorithms, they can track what posts users are liking, how much time they spend on each site, and engagement with other users and ads. The combination of all this data added together helps social media sites get an idea of the behavioral and personal characteristics of each of their users. Through this, social media platforms have a plethora of information on their users that they can sell to other companies looking to get more data on their potential customer profiles. This is often used in respect to company marketing efforts. Social media companies tend to use the aforementioned user data in order to generate customer profiles based on both demographic and behavioral factors. This is useful for companies wanting to get advertisements for their products specifically targeted to those who are most likely to purchase them. As such, social media platforms present themselves as a lucrative space to mine data and collect user information.^[3]

2010 Facebook Privacy Scandal

This relationship between data mining and social media was brought to light about a decade back during the controversial scandal involving Cambridge Analytica, a consulting firm from located in Britain, and Facebook. In short, the personal information and data of millions of users of Facebook was provided to Cambridge Analytica. After the collection of data from close to 87 million Facebook users was obtained, Cambridge Analytica provided analysis of this data that ultimately helped to aid the presidential campaigns of both Donald Trump and Ted Cruz during their run for presidency in 2016. It was also speculated that the data collected interfered with other sizable incidents including the Brexit Referendum. However, such speculations were not confirmed.^[4]

Ethical Concerns of Data Mining

While data mining has become a fairly regularly-used tool for many companies, there are those, including the general public, that raise concerns about the nature of this analytical intelligence. In one way, a worry that some have brought up is the relative ease in which companies are able to access the personal information of users. This has been specifically tied in relation to the flow of information between social media platforms and other companies. Most users take issue in the lack of transparency between such companies in providing proper explanations on what information is being collected, sold, and used by them and other businesses. As a result, many have stated that such uses of personal data go against certain FTC regulations and should be considered a breach of privacy^[5]. On the other hand, companies that use the data of users argue that it is up to the users to read the individual "Terms of Agreement" clauses to understand how and when their data is being used. Businesses state that data mining is a useful analytical tool utilized in marketing efforts that ultimately betters customers and provides companies the information needed to grow and develop.^[6]

References

1. ↑ Twin, A. (2023, January 20). What is data mining? how it works, benefits, techniques, and examples. Investopedia. Retrieved January 26, 2023, from https://www.investopedia.com/terms/d/datamining.asp
2. ↑ What is data mining: Definition, examples, tools, and techniques (for beginners). Georgia Tech Boot Camps. (2021, June 14). Retrieved January 26, 2023, from https://bootcamp.pe.gatech.edu/blog/what-is-data-mining/
3. ↑ Twin, A. (2023, January 20). What is data mining? how it works, benefits, techniques, and examples. Investopedia. Retrieved January 26, 2023, from https://www.investopedia.com/terms/d/datamining.asp
4. ↑ Twin, A. (2023, January 20). What is data mining? how it works, benefits, techniques, and examples. Investopedia. Retrieved January 26, 2023, from https://www.investopedia.com/terms/d/datamining.asp
5. ↑ Pedersen, J. S., & Wilkinson, A. (2019). The promise, application and pitfalls of Big Data. Big Data, 1–12. https://doi.org/10.4337/9781788112352.00005
6. ↑ van Wel, L., & Royakkers, L. (2004). Ethical issues in web data mining. Ethics and Information Technology, 6(2), 129–140. https://doi.org/10.1023/b:etin.0000047476.05912.3d