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News & Blog > Blogs: "Perspectives, Provocations & Initiatives" > Data mining: To Regulate or Not to Regulate?

Data mining: To Regulate or Not to Regulate?

Samirah Bello (MAGlob13 ) tells us more about data mining and asks you to think about whether further regulation is needed to mitigate potential harm, especially in the case of government data.
I-Stock 1279961075
I-Stock 1279961075

Data mining has been a topic of serious discussion in recent times. Particularly in light of the recent release of the Nexflix movie - The Social Dilemma. Individuals are said to be influenced and monitored by the digital trail they leave behind, which can be concerning and frightening. As a result, civil society is advocating for stricter regulations or boycotting. However, I wonder if we understand what data mining is, the good it offers, and the fact that there is already exist General Data Protection Regulations (GDPR) in many countries. Do we want stricter regulations?

Understanding data mining

To know if we want stricter data mining regulations, we need to understand data mining and its use. Data mining (as illustrated below) is the process of sifting through large data sets to identify correlations, anomalies, and patterns that can be used to forecast future events. This information is used in various ways to mitigate risk, increase revenues, improve customer relations, and reduce costs. To be precise, some believe mining was instrumental in establishing the principle of ‘work smarter, not harder.’ Because companies can target their advertisements to specific customers using mined data to reduce costs and time while increasing revenue and governments can ensure the security of their citizens and combat fraud within a shorter time.

Image above shows the data mining process. From i-stock 1169549198

Reasons why governments mine data

Over the last decade, the government’s ability to analyse and obtain recorded information about its citizens has increased dramatically, thanks to data mining. Governments mine citizens’ data for various reasons: research-driven, match-driven, target-driven, and event-driven.

  • Research-driven improves research, particularly in the health field, as evidenced by the UK NHS’s recent plan to collect data from patients’ GP medical records beginning September 1, 2021, with the stated goal of assisting in the discovery of better treatments and improving patient outcomes through research.
  • Match-driven algorithms are designed to see if a specific person has already been identified as a ‘person of interest.’ To put it another way, the purpose here is not to learn more about a suspect but to see if a specific person is a known suspect.
  • Target-driven refers to a search of records to find information about a certain target. This is sometimes accomplished by searching through ‘personal’ records such as phone, tax, Internet Service Provider (ISP) logs, and banking to learn more about specific persons suspected of engaging in illicit conduct.
  • Event-driven data mining aims to find the perpetrator of a past or future incident; unlike match-based and target-based data mining, this sort of data mining does not begin with a suspect in mind. So, for example, it can be used to hunt down a serial rapist.

Reasons why businesses mine data

Communication, retail, banking, insurance, and education mine data for many reasons in the corporate world. The competitive nature of the communication and retail sectors leads to customer data being mined and used to create targeted advertisements and campaigns that ensure positive customer interactions and successful sales and forecasting sales. While the banking sector heavily relies on automated algorithms and data mining to make sense of the financial system’s billions of transactions. This enables financial organisations to ensure they get the best return on marketing investments, detect fraud quickly, gain a bird’s eye view of market risks, and manage regulatory compliance. Insurance companies can better price products and create better options for existing customers while also encouraging new ones to sign up using data mining. Finally, data mining is used to track students’ progress in the education sector, enabling educators to provide more personalised attention where it is needed and develop early intervention strategies for groups of students who may require them. Additionally, data mining is critical in the commerical Artificial Intelligence (AI) space as it aids in the improvement of products and services.

Based on this, should we ensure stricter data mining regulations?

The benefits of data mining are obvious. For governments - target based programs can help in apprehending or developing cases against many criminals. Undoubtedly, match based programmes have kept many dangerous persons off planes and likely stopped others from attempting to board. Governments have used event-based data mining to detect money laundering, resolve identity theft cases, recover millions of dollars in fraud and immigrant smuggling operations. And research-based programmes have aided in the development of thousands of medical solutions. Data mining has aided in cost reduction in the business world, increased efficiency, and aided consumers in obtaining products they truly desire and possibly require without stress. It has also helped businesses develop products that are human-friendly, particularly in the AI space.

Despite all this good, data mining has resulted in a slew of harms, including government data inaccuracy, which has resulted in wrongful profiling, detention, and denial of employment, credit, boarding aeroplanes, or housing. Additionally, it has created unease among citizens in certain countries, as governments use data mining to combat terrorism. Some citizens are afraid to call Muslim friends or colleagues, while others are afraid to search for simple things online, such as Halal meat, for fear of being placed on the terrorist watch list. And in the business world, as data mining involves the handling of personally identifiable information, there are numerous privacy, security, manipulative, ethical, information misuse, discrimination, and aggressive marketing concerns. Users’ data is further mined for companies’ selfish reasons, used to manipulate people during elections, sold on black markets (which can be used to hack bank accounts or steal from banks), and used in scams. Additionally, individuals receive a large amount of unsolicited advertising and spam on their devices as a result of data mining, and individuals are at risk of kidnap for body parts or other items.

Do we want stricter regulations?

Will you, however, assert that the negative outweighs the positive? Although data mining aims to make the world a safer place, improve customer experience, and revolutionise research and development. However, the adverse effects affect you and me more than the government or companies. This leaves YOU to decide whether your data should be mined and advocate for the extent it should be regulated. Personally, I believe that data mining should be further regulated to include a YES or NO option when being prompted on cookies and individuals who choose No should still be allowed access to the site and individual who say YES should be paid for their data.

This is one a series of blogs supported by the IDS alumni office and written by current IDS students and PhD Researchers from academic year 2020-2021 Spring Term.

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